Energy security scholarship has expanded beyond fossil-fuel supply stability into a multi-dimensional discourse shaped by decarbonization, technology transitions, and geopolitical disruption. To characterize how scholarly attention propagates across themes, this study applies contextual topic modeling and time-series causal discovery to 6951 Scopus-indexed abstracts published in 2005Q1-2024Q4 (80 quarters). BERTopic identifies 29 interpretable topics, which are further organized into five macro-clusters using hierarchical clustering on semantic embeddings of topic summaries. Quarterly topic-prevalence series are then analyzed with Peter and Clark Momentary Conditional Independence (PCMCI) (maximum lag tau(max)=8; p < .01) as an exploratory tool for estimating directed, time-lagged conditional dependencies in discourse dynamics. Robustness is evaluated across five model specifications with progressively richer conditioning sets, including publication intensity and event indicators for the Paris Agreement (from 2015Q4) and the Russia-Ukraine war (from 2022Q1). Thirty-seven directed links remain significant across all specifications, indicating a densely coupled discourse system rather than isolated thematic silos. The water-food-energy nexus repeatedly appears as an upstream driver, while long-horizon links connect wind-integration discourse to geothermal development (lag 7) and coal-mining innovation (lag 8). A pre/post comparison (2005-14 vs 2015-24) shows relative re-weighting toward distributed integration, climate-risk framing, and data-driven forecasting, with relative dilution of biofuels and next-generation nuclear. The proposed framework provides reproducible lead-lag signals for agenda monitoring and anticipatory governance, while explicitly distinguishing discourse-level dependencies from real-world technological causation.
Heavy crude oils contain elevated concentrations of vanadium and nickel, which severely impair refining efficiency, accelerate catalyst deactivation, and increase environmental burdens during petroleum processing. Although nanoparticle-assisted and ultrasonic-based demetallization methods have been widely reported, the influence of nanofluid preparation route on metal removal efficiency has not been systematically isolated or quantified. This study presents a direct and controlled comparison between two alumina-based treatment routes for ultrasonic-assisted demetallization of East Baghdad heavy crude oil: a pre-prepared alumina nanofluid and a directly mixed nanoparticle-kerosene-surfactant system. Both systems were evaluated under identical ultrasonic conditions (40 kHz, 60 min) across a temperature range of 20-75 degrees C, enabling independent assessment of preparation methodology effects. The pre-prepared alumina nanofluid exhibited markedly superior demetallization performance, achieving vanadium and nickel removal efficiencies of 92% and 85%, respectively, compared with 83% and 72% obtained using the directly mixed system. This improvement is attributed to the formation of a stable and uniformly dispersed nanofluid, which ensures sustained accessibility of gamma-Al2O3 active sites and enhances adsorption of metal-porphyrin complexes under ultrasonic cavitation. Comprehensive physicochemical characterization using TEM, XRD, TGA, AFM, and BET analyses confirmed that the pre-preparation route preserves nanoparticle structural integrity while maximizing surface activity and mesoporosity. The principal novelty of this work is the experimental demonstration that nanofluid preparation route, rather than nanoparticle chemistry alone, constitutes a governing parameter in heavy crude oil demetallization efficiency. By decoupling formulation effects from operational conditions, this study establishes a design principle for sustainable nanofluid-based upgrading systems.
In this study, the catalytic potential of low-cost materials (i.e. olivine, dolomite, and red mud (RM)) for H2-rich gas generation through the air gasification of wood-plastic composite (WPC) was investigated. Among the low-cost materials, the use of RM resulted in the maximum H2 selectivity (25.81 vol%) and gas yield (65.80 wt%). This is attributed to the effect of Fe2O3 content in RM, which induced the formation of oxygen vacancies, enhancing coking resistance, and stimulating water-gas shift reaction during the gasification process. Using RM, increasing the temperature from 700 to 800 degrees C enhanced the gas yield and H2 selectivity due to the promotion of endothermic dry- and steam-methane reforming reactions. Increasing the equivalence ratio in the range of 0.2 to 0.3 decreased the H2 selectivity while promoting the gas and CO2 production because of strengthened oxidation reactions which is undesirable for H2-rich gas production. Further, loading 10 wt% Ni and the addition of promoters increased the gas yield along with H2 and CO selectivities, in particular, 5 wt% Ca-10 wt% Ni/RM catalyst showed the highest gas yield (77.72 wt%), and H2 selectivity (37.49 vol%). This suggests that the combined effects of Ni and promoters enhanced the activity, selectivity, and stability of catalyst for H2 production. Overall, air gasification using promoted Ni-based catalysts supported on low-cost materials like RM as a catalyst can provide a new technology to convert WPC into a more useful and high-energy density substance such as H2-rich gas.
Given the tourism sector's considerable environmental impact and economic relevance, assessing environmental sustainability is important. This is particularly relevant given the commitments established in the UN 2030 Agenda for Sustainable Development. A sample of 147 firms from 39 countries and Refinitiv data accessed via LSEG Workspace were used to construct an index of material environmental disclosure based on Sustainability Accounting Standards Board standards. Using a Generalized Method of Moments approach, we find that environmental disclosure is positively associated with market capitalization, with a stronger relationship observed in common law jurisdictions. These findings underscore the value of managerial proactivity in adopting materiality-based sustainability disclosures and highlight the role of regulatory initiatives in fostering such practices, which may strengthen investor confidence and enhance firm value.
Agriculture is a critical sector contributing significantly to greenhouse gas (GHG) emissions in Europe, primarily through land-use change, fertilizer application, and energy-intensive practices. This study provides an in-depth exploration of the factors influencing GHG emissions from agriculture in Europe (30 countries) from 1995 to 2022, utilizing a diverse range of variables, including agricultural value added (AVA), cereal yield (CY), total agricultural land (TAL), and total energy consumption agriculture (EGYTA). By implementing a composite index derived from principal component analysis and two interaction terms (AVA*CY and AVA*EGYTA), the study provides a comprehensive understanding of the relationships between these variables. The cross-sectionally augmented autoregressive distributed lag technique elucidates the dynamics influencing agricultural GHG (A-GHG) emissions. Key findings indicate that in the long run, a 1% increase in AVA is associated with approximately a 0.07% decrease in A-GHG emissions, while TAL exhibits a positive elasticity of 0.334. CY, EGYTA, and AVA*CY also positively correlate with A-GHG emissions, contributing to elevated GHG emission levels in the sector. It implies that increasing reliance on fertilizers and escalating energy consumption in modern farming are identified as significant concerns. Conversely, AVA, gross domestic product, and AVA*EGYTA show a negative relationship, indicating their potential to mitigate emissions and foster more sustainable agricultural practices. The implications of land clearance on carbon and nitrogen cycles highlight the major role of AVA as both a symbol of economic prosperity and a potential environmental challenge. Based on the findings, the study recommends promoting sustainable farming techniques, encouraging energy efficiency in agriculture, and supporting value-added agricultural production to reduce emissions while sustaining economic productivity.
In response to the climate change problem, ensuring a transformation to green economies is highly critical. Therefore, countries have been trying to make their economies decarbonized by taking various measures. Accordingly, this study examines the USA case by using carbon dioxide emissions (load capacity factor) as the main (robustness) environmental proxy, considers energy-related research and development (R&D) investment types as main explanatory variables, controls income and energy utilization sub-types, and performs a novel kernel-based least squares (KRLS) model on data from 1974 to 2022 to apply a marginal effect analysis. The results show that (i) R&D investment sub-types have an insignificant effect on carbon dioxide (CO2) emissions; (ii) income structure does not contribute in greening economy; (iii) among energy utilization sub-types, only renewable energy has a decreasing effect on CO2 emissions, whereas nuclear and fossil energy sub-types have a reverse ones; (iv) the effects of the factors on CO2 emissions differentiate across percentiles and estimation models; (v) the robustness of the empirical results are verified based on alternative indicator; (vi) the KRLS model has a high estimation capability around 99.7%. Hence, the empirical results reveal the critical role of renewable energy use, while the current R&D investment structure, energy utilization, and income are not supportive of a green economy in the USA because these do not provide a decrease in CO2 emissions.
The direct transformation of seawater into hydrogen fuel offers a sustainable pathway toward clean energy generation, yet it remains hindered by competing ionic reactions and photocathode instability. Here, we report a tellurium dioxide-decorated polypyrrole (TeO2-PPy/PPy) nanostructured photocathode that overcomes these barriers by combining broad optical absorption, efficient charge separation, and structural robustness within a single hybrid design. The composite exhibits strong light absorption with an optical band gap of 2.25 eV and a well-defined crystalline framework, enabling efficient photocarrier generation. Electrochemical evaluation was performed in both natural seawater collected from the Red Sea and a laboratory-prepared artificial seawater analog. Under dark conditions, both electrolytes yielded a negligible current density of approximately -0.05 mA cm-2, whereas illumination produced enhanced photocurrent densities of -0.072 and -0.08 mA cm-2 for artificial and natural seawater, respectively. These photocurrents translate into hydrogen evolution rates of 20.0 and 19.5 & micro;mol h-1 for a 10 cm2 photocathode, in excellent agreement with theoretical predictions derived from Faraday's law. Optical-filter-dependent measurements confirm the broadband photoresponse of the electrode, while repeated operational cycles demonstrate remarkable durability and resistance against chloride-induced corrosion-a critical challenge in seawater electrolysis. Collectively, these results establish TeO2-PPy/PPy as a robust and scalable photocathode platform for direct seawater-driven hydrogen generation. Beyond the immediate application, this molecular design approach provides a versatile blueprint for engineering polymer-oxide heterostructures with tailored interfacial energetics, paving the way for the development of next-generation solar-to-hydrogen energy conversion systems.
We examine whether government environmental incentives can resolve the underinvestment problem in corporate sustainability by exploiting China's Green Factory Certification program as a quasi-natural experiment. Using difference-in-differences estimation on Chinese listed firms from 2010-2024, we find that certification increases Environmental, Social, and Governance (ESG) performance by approximately 3% relative to the control group. The effect operates through two complementary channels: green innovation investment that generates firm-specific capabilities, and operational efficiency gains that improve green total factor productivity. The effects are significantly stronger for non-state-owned enterprises and firms with lower environmental public concern, indicating that market-based incentives amplify policy effectiveness. Certification most strongly improves environmental performance, followed by governance and social dimensions, suggesting spillover effects through enhanced organizational capabilities. Our findings demonstrate that environmental certification transcends compliance by building capabilities that address coordination failures in ESG investment, providing theoretical foundations for understanding how government intervention drives corporate sustainability transformation.
Improving carbon emission efficiency (CEE) in the construction sector is crucial for achieving low-carbon buildings and ecological civilization goals. To clarify the driving mechanisms of low-carbon development, this study measures the CEE of China's construction industry using the Super-SBM model and analyzes its spatiotemporal drivers via the Geographically Temporally Weighted Regression model. Based on the analysis, the approach enabled us to analyze temporal and regional variations in CEE across regions in 30 provinces in China from 2013 to 2022. It is found that the Carbon Emissions of the Construction Industry (CECI) experienced a stage from stable growth to slowing growth. From the spatial perspective, the CECI show a typical distribution pattern, which is higher in the eastern region, lower in the western region, and middle in the central region. Regarding the driving mechanisms, technical factors and demographic factors display distinct impacts. Technological advancement serves as a pivotal positive driver, enhancing efficiency through the dissemination of green construction technologies and energy-saving processes. Conversely, demographic factors generally impose constraints on CEE across most regions. This is primarily attributed to the escalating demand for infrastructure and the intensive resource consumption associated with population agglomeration. These results suggest that accelerating industrial upgrading in the construction sector could reduce reliance on high-carbon industries. The findings provide empirical evidence and policy insights for China's low-carbon transition, including differentiated regional strategies and enhanced interprovincial collaboration.
Most studies explaining green and low-carbon consumption behavior mainly focus on a single influence at the individual psychological level, while ignoring the multilevel interaction between macro situational factors and individual factors in nested cities. Therefore, based on large-scale survey data and national statistical data from 35 cities in China, this article integrates individual-level psychological factors with urban-level background factors, employs a multilevel structural equation model (MSEM) to investigate the key influencing factors and mechanisms of individual green and low-carbon consumption behavior. It examines consumption behavior with four different characteristics: green food consumption, green low-carbon travel, green energy consumption, and green living. The results indicate that at the individual level, consumer perceived value and perceived behavioral control actively promote four types of green and low-carbon consumption practices, with conditional value having the most extensive promotion effect. At the urban level, environmental pollution, economic development, and social development can all encourage low-carbon consumption behavior. Moreover, the level of social development positively moderates the relationship between environmental pollution perception and green consumption intention. The level of environmental pollution positively moderates the relationship between environmental attitudes and green low-carbon travel. However, the level of economic development suppresses the promotion effect of environmental pollution perception on environmental attitudes. Therefore, in the formulation of environmental policies, full consideration should be given to the development status of each region. To strengthen regional infrastructure and improve the incentive system for green and low-carbon markets, it is essential to utilize information dissemination channels flexibly.
In the digital economy era, Central Bank Digital Currency (CBDC), as a new type of financial infrastructure, will also have an important impact on green development. In this paper, using the e-CNY pilot policy as a quasi-natural experiment, we explore the effect of e-CNY issuance on urban green innovation and its intrinsic mechanism by constructing a staggered difference-in-differences (DID) model. We found that the e-CNY pilot policy significantly increases the level of urban green innovation, and this effect gradually increases during the study period. The policy shows obvious regional heterogeneity, with more prominent policy effects in peripheral urban circles, central and eastern regions, resource-based areas, regions with high environmental attention, and regions with low digitalization levels. Mechanism analysis indicates that the e-CNY pilot policy enhances urban green innovation by improving digital financial level, optimizing resource allocation efficiency, and narrowing the digital divide. Further analysis reveals that green capital investment, the strength of intellectual property protection, and the level of green certification technology play significant moderating roles in this process. In addition, e-CNY pilot policy has a significant spatial spillover effect. Taking the e-CNY pilot policy as an entry point, this paper reveals the potential value and realization path of the CBDC in the field of green innovation. It provides both a theoretical basis for policy makers to enhance the e-CNY functional ecosystem and optimize pilot project designs, as well as practical guidance for accelerating China's green and low-carbon economic and social transformation amid the digital economy wave.
This study examines the relationships among technological innovation, energy efficiency, and reductions in carbon emissions, while accounting for the moderating effects of energy- and carbon-intensive sectors. Using longitudinal panel data from South Korea spanning 2011 to 2023, this study provides evidence that general patents and green patents, as a proxy for technological innovation, significantly increase energy efficiency and reduce carbon emissions. The results of this study also confirm that energy efficiency partially mediates the association between technological innovation and emissions reductions. Notably, these positive effects are stronger in energy- and carbon-intensive sectors. This study is novel in that it explores the interplay among patents, energy efficiency, and carbon emissions by simultaneously considering mediation and moderation mechanisms. Our findings provide managerial and policy implications for enhancing corporate competitiveness when transitioning to a low-carbon economy.
Effective carbon management and sustainable development require a comprehensive understanding of the complex spatial-temporal dynamics and network characteristics of embodied carbon transfers (ECTs) between regions. This research utilizes a multi-regional input-output model, complex network analysis, and a geographical detector to investigate the inter-regional ECT in China. The results reveal that northern energy-producing provinces act as "suppliers" of embodied carbon within the transfer network, while economically developed eastern regions emerge as both significant "consumers" and "re-exporters." The central region is an intermediary bridge connecting the south and the north. Additionally, 10 key ECT pathways have been identified, illustrating the complexity and frequency of transfers. Energy production and manufacturing are the main contributors to the ECT network, whereas service sectors such as transportation, warehousing, and postal services play a significant role in the final consumption. Finally, the synergistic effects among various factors, notably the combined influence of urbanization rates and economic openness, coupled with education levels and green innovation initiatives, significantly influence the inter-regional ECT. These findings offer valuable insights into the equitable allocation of carbon responsibilities and collaborative governance of emission reduction strategies across regions, providing policy guidance to foster green and sustainable development.
Escalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input-output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for T & uuml;rkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for T & uuml;rkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20-30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.
The aim of this article is to analyse the complex contractedness and interaction between environmentally efficient (green) and energy-intensive (non-green) cryptocurrencies and carbon markets during major global crises, with a focus on implications for carbon market stability and sustainable portfolio design. Based on climate finance and transition-risk perspectives, the analysis focuses on the environmental impact of digital assets and the transmission of volatility to carbon markets during periods of systemic disruption. Using daily data covering the COVID-19 pandemic, the Russia-Ukraine war, and the Israel-Gaza conflict, the research applies the Diebold-Yilmaz spillover framework within the Markov regime-switching models to assess the time-varying volatility transmission and crisis-specific regime shifts. The results show that cross-market interconnectedness reaches its highest level during the pandemic (76.68%) and declines during subsequent geopolitical conflicts, reflecting changes in global liquidity conditions, investor sentiment, and the synchronization of shocks. Green cryptocurrencies display diverse patterns: some generate lower average spillovers, while others, such as ALGO, become significant transmitters of volatility under stress. The study contributes to the emerging literature by differentiating spillover effects according to the environmental classification of crypto assets, adopting a multi-crisis and regime-dependent analytical perspective, and linking volatility transmission to climate policy dynamics and ESG-related financial mechanisms.
This study highlights the critical role of green macro-prudential policy (GMPP) in green material monitor (GMM). Using data from 64 countries from 2000 to 2022, the analysis uses the panel vector autoregressive method and the dynamic panel threshold model. The results suggest that GMPP received positive shocks from GMM in the first period, and its responses turned negative from the second period onwards. In contrast, the impact of GMPP on GMM was negative and became more significant in the first four periods. Its negative impacts decreased to zero by the end of the period. Furthermore, the threshold quantity of green macro-policy is 87.5%. Below the threshold, GMPP hurts total resource usage (green accounting), while above the threshold, this effect becomes less significant. Therefore, central banks should link preferential credit and refinancing rates to verified environmental disclosures, thereby motivating firms to adopt transparent GMM. Such a GMPP aligns financial incentives with sustainability reporting and strengthens accountability in the financial system.
Carbon-negative technologies (CNTs) are vital to achieving net-zero climate goals. However, regional disparities in CNT innovation capacity remain insufficiently understood. This study maps the innovation landscape of CNTs across 18 European countries from 2013 to 2023 using a novel multidimensional, data-driven approach. A real-coded accelerated genetic algorithm is integrated with projection pursuit to construct a high-dimensional evaluation model capable of handling nonlinear, nonnormal data distributions. Thirteen indicators across innovation input, output, and environment are selected to quantify national innovation capacities. Results show that Germany, France, and Italy consistently outperform others in CNT innovation, driven by high R&D investment, patent activity, and strong economic foundations. In contrast, countries like Portugal, Slovenia, and Luxembourg exhibit lower innovation capacities. Cluster analysis reveals a clear stratification into high-, medium-, and low-performing countries, highlighting structural differences in policy support and industrial development. It reveals the "club convergence" characteristic of negative carbon technology innovation capabilities across 18 countries, meaning these nations can be clearly divided into three hierarchical innovation echelons (high, medium, and low) rather than exhibiting continuous geographical agglomeration. Further mechanism analysis indicates significant differences in the innovation-driven factors among countries of different echelons: leading nations are driven by their macroeconomic foundation, while catching-up nations rely more heavily on government R&D investment and human resources. Temporal trends indicate overall growth in innovation capacity, with temporary declines linked to Brexit and the COVID-19 pandemic. This research contributes a robust methodological framework and a comparative evaluation of CNT innovation in Europe, offering critical insights for policymakers and stakeholders aiming to strengthen cross-border collaboration and accelerate the deployment of CNTs.
Brazil's commitment to cut emissions significantly by 37% and 50% by 2025 and 2030, respectively, and reach carbon neutrality by 2050 reflects the importance of renewable energy, green finance, and technology as part of Brazil's climate strategy. This paper assesses the trilemma of financial development, renewable energy, and green technology in relation to Brazil's environmental quality, utilizing time-series data spanning 32 years (1990-2021). The autoregressive distributed lag (ARDL) framework was adopted, incorporating interaction terms between financial development and renewable energy consumption, as well as financial development and green technological innovation, to provide a nuanced perspective of how green finance is a crucial player supporting Brazil's transition toward a lower carbon economy. The results show that renewable energy consumption significantly shrinks carbon emissions in the long run, reflecting the ecological strength of Brazil's clean energy matrix, while underscoring the need to diversify beyond hydropower. Alone, financial deepening exhibits an ambiguous effect on emissions, which evidently becomes environmentally beneficial when interacted with renewable energy and green innovation, thus illustrating the enabling role of green finance policies, ESG frameworks, and targeted climate finance. Green technological innovation shows only marginal long-run improvements in environmental quality, reflecting structural gaps in Brazil's innovation system. Economic growth still increases emissions, indicating that Brazil has not yet decoupled growth from environmental degradation. Overall, the results affirm that coordinated improvement in financial development, green innovation, and renewable energy is pivotal for actualizing Brazil's climate goals and bolstering its pathway toward a sustainable, low-carbon economy.
This study presents the synthesis of a chabazite photocatalyst (Cu/TiO2/SAPO-34) using an innovative hydrothermal method for the rapid degradation of pharmaceutical and organic pollutants. The material was characterized using scanning electron microscope, energy dispersive spectroscopy, X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), UV-vis diffuse reflectance, and photoluminescence spectroscopy for morphology, structure, molecular bonding, and optical properties. The Cu/TiO2/SAPO-34 was photoelectrochemically characterized using linear sweep voltammetry (LSV), electrochemical impedance spectroscopy (EIS), Photocurrent (PC) transient curves, and Mott-Schottky (MS) analysis. The pharmaceutical and organic pollutant removal ability of the photocatalyst was investigated through a paracetamol and methylene blue photodegradation test, using ultraviolet radiation. The Cu/TiO2/SAPO-34 photocatalyst exhibited superior electrochemical, light absorption, structural, and photocatalytic properties compared to TiO2. It demonstrated reduced charge transfer resistance (2007 vs. 3931 Omega), higher photocurrent density (46 vs. 9 mu A/cm2), and improved charge migration and optically generated charge carrier separation, leading to improved photoelectrochemical performance. Structural analysis confirmed the formation of the composite with a larger average crystallite size (17.56 vs. 13.11 nm for TiO2). FTIR and EDX analyses verified the presence of Cu, TiO2, and SAPO-34 in the mix. Optical analysis showed reduced bandgap energy (2.61 eV vs. 3.09 eV for TiO2), enhanced UV and visible emissions, and early infrared absorption. The composite outperformed TiO2 in degrading pharmaceutical and organic contaminants, showcasing its potential for advanced photocatalytic and photoelectrochemical applications. The material can suppress biodegradation, preserve the natural world, enhance solar energy absorption, self-clean medical equipment, and purify wastewater from pharmaceutical contaminants.
Our research addresses the increasing concern of interdependency in the global financial markets, especially the stock market of G20 nations (Australia, Argentina, China, France, Japan, and the USA) and the WTI (West Texas Intermediate) oil market between January 2017 and September 2025. The dynamic connectedness approach and quantile VAR are a strong check as we use them to determine the spillover effect in various market conditions. We find that the spillover effect in bullish and bearish market conditions is strong with the Total Connectedness Index (TCI) of 71.98% and 70.25%, respectively. In the normal case, the highest connectedness effect is achieved at tau=0.5, and this result is consistent with the dynamic TCI of 39.12%. Based on the varying market conditions, USA, Australia, and France are strong net transmitters, but WTI is a net receiver in a bullish and bearish market situation, then Argentina, China and Japan, respectively depending on the return spillover effects. The USA, France, and Australia can strengthen their leading position as a net transmitter in the volatility spillover. Japan is a net transmitter in bearish market conditions only in the volatility series, and it is a net transmitter in bullish market conditions only in the return series. This became particularly clear during the global epidemic, the war between Russia and Ukraine, and the ongoing tariff war. In general, our study points to the increased role of the USA stock market in world markets concerning WTI. These findings can be used by investors and policymakers to maximize returns and ensure market stability.
