The double-edged net: A wavelet coherence analysis of artificial intelligence's perverse effect on carbon dioxide emissions in global fisheries.

Identifying impact factors of long-term ecological vulnerability using multivariate wavelet coherence analysis

Semi-open centrifugal pumps are widely used in energy systems, particularly for handling multiphase flows in industrial, municipal, and energy generation applications. While the semi-open impeller design offers advantages in managing solid–liquid mixtures, it also presents challenges such as leakage flow and flow instability, which can significantly affect both energy efficiency and overall operational performance. Despite substantial research utilizing conventional numerical approaches, such as Eulerian-Eulerian and Euler–Lagrange models, a comprehensive understanding of the intricate interactions between solid particles and the fluid remains insufficient, particularly regarding the impact of particle behaviour on fluid dynamics and system energy dissipation characteristics. This work aims to investigate the two-phase flow behaviour and energy dissipation mechanisms under the influence of leakage flow in semi-open impellers by integrating Computational Fluid Dynamics (CFD) with the Discrete Element Method (DEM), coupled with normalized scale-averaged wavelet spectrum and coherence analysis. The results reveal that increasing the particle volume fraction intensifies particle-blade collisions, disrupting vortex structures and compromising hydraulic stability, while larger particles exacerbate flow disturbances, leading to efficiency losses of up to 12.90%. Furthermore, the energy fluctuations generated by particle-fluid interactions predominantly manifest as high-frequency pressure pulsation signals. A mutual suppression effect between the pressure pulsation signals and particle energy dissipation is observed at the blade passing frequency. These findings offer critical insights into the influence of particle dynamics on pump performance and provide valuable guidance for optimizing pump design to improve operational stability and energy efficiency in solid–liquid flow applications.

Decoding the environmental effects of green finance, renewable energy, financial development, and globalization through quantile on quantile and wavelet coherence analysis

This study investigates the dynamic relationship between carbon dioxide (CO₂) emissions, economic growth, energy consumption, and industrial output in South Africa from 1990 to 2022. Employing a combined approach of Autoregressive Distributed Lag (ARDL) modeling and wavelet coherence analysis, the research captures both long-run elasticities and localized time-frequency dependencies among the variables. The findings reveal that economic growth, energy consumption, and industrial activity significantly drive CO₂ emissions, highlighting the environmental challenges accompanying South Africa’s development path. Policy implications emphasize the need for integrated strategies focusing on energy efficiency, renewable energy adoption, and green industrialization to achieve sustainable growth. The study contributes to the literature by providing nuanced insights into the temporal and spectral dimensions of emissions dynamics, offering valuable guidance for policymakers pursuing climate change mitigation in emerging economies.

This study investigates the relationship between geopolitical risks and inflation in Turkey over the period 1985-2023, employing a time-frequency analysis approach. The study utilizes the Global Geopolitical Risk Index (GPR) as the indicator of geopolitical risk and the Consumer Price Index (CPI) as the indicator of inflation. Employing continuous (CWT), cross (XWT), and coherence (WTC) wavelet analyses, the study examines the intrinsic fluctuations of the variables, their co-movements, and correlations within the time-frequency domain. The findings from the wavelet analysis indicate a generally positive relationship between geopolitical risks and inflation in Turkey, which fluctuates across time and different frequencies. Notably, periods such as the Gulf War in the early 1990s, the 2001 economic crisis, and the post-2018 period exhibited a strong association between increased geopolitical risks and inflation. During these periods, significant co-movements (XWT) and high coherence (WTC) were identified between the variables, alongside short-to-medium-term cycles. In contrast, during the period of relative stability in the mid-2000s, the influence of geopolitical risks on inflation diminished. However, following 2018, the relationship has strengthened again, concurrent with an escalation in geopolitical risks faced by Turkey. The study's conclusions underscore the significance of considering geopolitical risks, exchange rate stability, energy and food security, the management of inflation expectations, and structural reforms in addressing inflation. The research contributes uniquely to the literature on the Turkish economy, by presenting a time-frequency domain analysis.

Purpose Financial markets are greatly impacted by investor sentiment, especially fear, which heightens uncertainty and leads to quick decisions during volatile times. This study delves into the co-movement and directional spillover of fear sentiment across different quantiles, focusing on implied volatility indices of the USA, developed, emerging and Indian stock markets. Design/methodology/approach To achieve this, we employed wavelet coherence analysis and the quantile VAR model for the period from 2011 to 2023, enabling us to capture the interaction among fear indices across normal and high-volatility states. Findings The study reveals dynamic co-movement of sentiment among the markets with substantial spillovers under extreme quantiles. The Indian stock market is extremely susceptible to fear sentiment originating from the USA. The spillover of fear sentiment is stronger than the spillover of greed, indicating asymmetry in sentiment spillover. The study further underscores the US stock market's role in fear transmission. Originality/value This study highlights how extreme events impact the spillover of fear among developed and emerging stock markets by giving special attention to the Indian stock market.

The Gravity Recovery and Climate Experiment (GRACE) tracks drought events by detecting changes in the global gravitational field and capturing abnormal information on the reserves of surface water, soil water, and groundwater, which makes it possible for a more comprehensive and unified global and regional monitoring of groundwater drought. This study adopted the gravity satellite GRACE data and combined it with the hydrological model dataset. Additionally, we assessed the temporal evolution and spatial pattern of groundwater drought in the Yangtze River Basin (YRB) and its sub-basins from 2003 to 2022, determined the change points of the hidden seasonal and trend components in groundwater drought, and identified the direct/indirect driving contributions of the main climatic and circulation factors to groundwater drought. The results show that (1) as a normalized index, the groundwater drought index (GDI) can reflect direct evidence of any surplus and deficit in groundwater availability. During the study period, the minimum value (−1.66) of the GDI occurred in July 2020 (severe drought). (2) The average value of GDI in the entire basin ranged from −1.66 (severe drought) to 0.52 (no drought). (3) The average Zs values (Mann–Kendall Z-statistic) of GDI were −0.23, −0.16, −0.43, and 0.14, respectively, and the proportions of areas with aggravated drought reached 65.21%, 61.05%, 89.70% and 43.67%, respectively. (4) Partial wavelet coherence analysis can simultaneously reveal the local correlations of time series at different time scales and frequencies. Based on partial wavelet analysis, precipitation was the best factor for explaining the dynamic changes in groundwater drought. (5) The North Pacific Index (NPI), the Pacific/North American Index (PNA), and the Sunspot Index (SSI) can serve as the main predictors that can effectively capture the drought changes in groundwater in the YRB. The GRACE satellite can provide a new tool for monitoring, tracking, and assessing groundwater drought situations, which is of great significance for guiding the development of the drought early warning system in the YRB and effectively preventing and responding to drought disasters.

Purpose This study investigates the dynamic relationship between petroleum consumption and carbon dioxide (CO2) emissions in India, highlighting their implications for environmental sustainability and policy formulation. Design/methodology/approach The analysis applies the wavelet coherence methodology to monthly data spanning April 1998 to June 2025. This approach allows for the exploration of time–frequency dependencies and the detection of short, medium and long-term linkages between petroleum consumption and CO2 emissions. Additionally, for complementary validation approach, rolling correlation is used. Findings The results reveal a consistently strong positive association, particularly across medium-to long-term cycles, with variations in petroleum consumption frequently preceding changes in CO2 emissions with also evidence that emissions often lead consumption. Short-term fluctuations are less stable, though significant events such as the COVID-19 pandemic demonstrate clear simultaneous impacts on both variables. These findings confirm the substantial contribution of petroleum consumption to India's CO2 emissions profile indicating a persistent positive (in-phase) relationship. Practical implications The evidence offers valuable insights for policymakers seeking to regulate petroleum demand, reduce carbon intensity and align energy strategies with India's environmental and sustainability goals. Targeted interventions in petroleum consumption management could play a pivotal role in achieving long-term decarbonization objectives. Originality/value This study is among the few to apply wavelet coherence analysis to the petroleum consumption–emissions nexus in India, providing novel evidence on the time-varying dynamics of energy–environment relationship using monthly data from the Indian context. The findings contribute to a deeper understanding of how energy demand patterns affect carbon emissions, thereby informing sustainable energy and climate policy.

This study examines the dynamic co-movement and interconnectedness of the Metal, Energy, ‎Carbon, and Artificial Intelligence (AI) markets to understand their evolving interactions in the ‎context of technological progress and sustainability transitions. The study employs wavelet coherence analysis to investigate the interactions between these markets across various time scales, encompassing both short-term variations and long-term trends. The results ‎demonstrate substantial long-term coherence, especially between Energy and Carbon, as well ‎as between Carbon and AI, signifying robust and enduring interdependencies. Medium-term ‎correlations demonstrate modest variability, likely influenced by market restrictions and ‎innovation cycles, whereas short-term linkages seem more unstable, reflecting acute shocks ‎and developing technologies. This paper presents new empirical evidence about the increasing ‎integration of financial and resource-based markets, highlighting the impact of AI ‎advancements and environmental issues on conventional sectors. The study enhances ‎comprehension of cross-market behavior, providing significant insights for investors, ‎policymakers, and researchers investigating market predictability, risk management, and ‎sustainable economic planning‎.

ABSTRACT Climate-related uncertainty is increasingly shaping investment, planning and adaptation decisions, yet its interaction with the scientific knowledge that supports climate-resilient development remains insufficiently examined. This study investigates how multiple policy- and market-based uncertainty measures relate to the evolution of research productivity and impact in climate- and energy-relevant fields. Using a corpus of 8,540 Scopus-indexed publications produced over roughly three decades, we construct quarterly bibliometric indicators and link them to Energy-Related Uncertainty (EUI) indices for 28 countries and two global aggregates, along with complementary climate and oil-market uncertainty proxies. Combining static and rolling Granger causality with wavelet coherence, we assess when uncertainty predicts changes in research activity and when scientific outputs feed back into uncertainty measures. Two broad patterns emerge. EUI predicts at least one research indicator in 18 countries, while research indicators predict EUI movements in 21, with bidirectional links in 15 cases. These associations are strongest for citation-weighted metrics and appear mainly over multi-year frequencies, suggesting that uncertainty–knowledge interactions unfold gradually rather than through short-lived shocks. Together, the results indicate that uncertainty can stimulate knowledge creation, while sustained research capacity contributes to more stable and adaptive policy environments.

Patients with postural orthostatic tachycardia syndrome (POTS) experience disabling symptoms such as brain fog related to reduced cerebral perfusion. The objective of this study is to determine the mediating role of carbon dioxide in the relationship between stroke volume and cerebral blood flow. A total of 15 female patients with POTS underwent head-up tilt testing under two conditions: with lower-body compression (higher stroke volume) and without (lower stroke volume). We analyzed cerebral blood flow velocity, respiratory, and cardiovascular responses using linear mixed-effects and mediation models to examine stroke volume-cerebral blood flow interactions. Granger causality and wavelet coherence assessed cerebral autoregulation. Lower-body compression attenuated the reduction in stroke volume (-34ml versus -23ml; p < 0.01), end-tidal CO2 (-6.4mmHg versus -3.2mmHg; p < 0.01), and mean middle cerebral artery blood flow velocity (-11.2cm/s versus -4.2cm/s; p < 0.01) during tilt. Mediation analysis revealed that carbon dioxide completely mediated the relationship between stroke volume and middle cerebral artery blood flow velocity, with a significant indirect effect (0.18cm/s/ml, 95% confidence interval (CI) 0.058-0.33) and a nonsignificant direct effect (0.04cm/s/ml, p = 0.5). Compression attenuated the association between stroke volume and carbon dioxide (-0.07mmHg/ml; 95% CI -0.12 to -0.010; p = 0.02), as shown by the linear mixed-effect model, and reduced the directional influence of blood pressure on cerebral blood flow (ΔGranger causality: 0.12 (0.05-0.18) versus 0.05 (0.02-0.08); p < 0.01). Reduction in stroke volume leads to reduced cerebral perfusion in POTS, an effect likely mediated by decreased carbon dioxide.

Abstract Fairall 9 is among the most massive Seyfert galaxies exhibiting a strong soft X-ray excess, but it is challenging to probe soft X-ray reverberation lags (if any) due to the long intrinsic timescales expected from its large black hole mass of ∼2.55 × 108 M⊙. We fit five XMM-Newton spectra of Fairall 9 using the hybrid reXcor model taking into account both hot and warm corona. The soft excess is explained by a combination of a physically motivated warm corona and the disc reflection. Then, we perform a wavelet coherence analysis of the light curves between 0.3–1 and 1–4 keV bands. The spectral fits are consistent with a rapidly spinning black hole (a = 0.99), a warm corona with optical depth ∼10–30, and a hot lamp-post corona located at either 5 or 20 rg. This configuration supports a coexisting hot and warm corona scenario, allowing the disc to extend almost to the event horizon. Our wavelet analysis on combined observations reveals signatures of transient soft X-ray lags, confined to specific time-frequency intervals. The earlier observations exhibit more variable and transient lag behavior. In contrast, the later observations display more persistent soft X-ray lags at the frequencies of ∼9 × 10−6–2.5 × 10−5 Hz, with amplitudes reaching ∼1000 s. The results indicate a progressively stable disc-corona configuration in later observations. Given the mass and geometry of Fairall 9, the observed soft lags appears plausibly consistent in both size and timescales with expectations from X-ray reverberation.

ABSTRACT Landslide activity in mountainous regions is increasingly driven by cumulative precipitation, especially in slow-moving landslides where precursory signals are subtle and delayed. Therefore, this requires an approach that not only links precipitation to deformation, but also uses multi-scale decomposition to resolve the layered temporal signals driving the evolution of deep-seated landslides. To address this challenge, this paper develops an integrated approach that combines Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) with multi-scale signal decomposition techniques to analyse. Firstly, nearly a decade of Sentinel−1 ascending and descending data is used to derive east-west and vertical deformation. Subsequently, Principal Component Analysis (PCA), Independent component analysis (ICA), K-means clustering, and wavelet analysis are then applied to extract dominant motion features and quantify the precipitation-deformation coupling. Results reveal strong interannual‑scale wavelet coherence between deformation and precipitation, with precipitation typically leading deformation by 50−80 days. Furthermore, after applying wavelet decomposition, precipitation and deformation maintained consistently high Wavelet Transform Coherence (WTC) over an extended period. Vertical deformation shows sustained precipitation-driven behaviour, particularly during the three months preceding the June 2017 failure, while east-west deformation exhibits localized. This modular approach provides transferable insights into the spatial and temporal variability and the delayed hydro-mechanical responses of precipitation-driven landslides.

ABSTRACT Accurate prediction of water turbidity is important for effective water quality management. However, it is a challenge task due to dynamic hydrological patterns and seasonal variability. The present study aimed to evaluate eight machine learning-based models for predicting average seasonal water turbidity . The MLPANN model presented the most accurate results among the employed techniques (RMSE: 3.047 FNU, R2: 0.932), while the performance of the models fluctuated among the seasons: GMDH, XGBoost, GBR, and RF presented the best outcomes for spring (R2: 0.941), summer (R2: 0.956), autumn (R2: 0.942), and winter (R2: 0.967), respectively. Diebold-Mariano tests confirmed significant performance differences between the models (e.g. CART vs. RF, p = 0.019), with river discharge identified as the most influential input variable (SHAP value: ~8, correlation: 0.70). Time-series analysis highlighted MLPANN’s robustness in tracking trends but exposed underestimation of extreme turbidity events (>80 FNU) and overestimation at low values (<5 FNU), with wide uncertainty intervals. Wavelet coherence further confirmed the multiscale influence of river discharge, with strong annual coherence (seasonal cycles) and intermittent short-term effects.

The study examines the dynamic linkages among financial development, natural resources, green technologies, trade openness, and economic growth in the United States from the period of 1970 to 2021. Employing a multiscale frequency framework that integrates wavelet coherence and frequency-domain causality, the analysis captures both the directional and persistence of relationship across short-, medium-, and long-term horizons. The results show the financial development initially boosts natural resources use and growth but gradually transition toward supporting technological innovation and efficiency improvement. Green technologies exert a strong long run casual influence in decoupling growth from resource dependence, while trade openness displays limited and cyclical effects. This study makes the primary contribution first it extend time frequency research beyond emerging economies by focusing on advance market-based system, revealing how financial cycles interact with environmental transition, second it develops a unified multivariate framework to uncover conditional and directional dependencies, third it provides policy relevant linking to financial sector, technological sector and resource sustainability within the context of the United States carbon neutrality agenda and the inflation reduction act. The findings underscore that sustainable finance in advance countries requires aligning capital markets with innovation led low carbon transformation pathways.

Environmental sustainability is a central concern in environmental economics, yet the effects of institutional quality and macroeconomic risks on sustainability outcomes remain underexplored, particularly in developed economies. This study examines how economic policy uncertainty (EPU), political risk (PRI), and governance quality (GOV) influence environmental sustainability in Canada, using the load capacity factor as a proxy. Utilizing quarterly data from 1990 to 2022, we apply the quantile-on-quantile regression method to capture heterogeneous and nonlinear relationships across different levels of environmental performance. Robustness is ensured through wavelet coherence analysis. The results reveal that EPU positively affects sustainability at higher quantiles, possibly due to precautionary shifts in policy or investment behavior. PRI also contributes positively in high-risk settings, reflecting the role of political institutions in environmental governance. Strong governance exhibits a consistently favorable impact across quantiles. Moreover, environmental innovation strengthens the positive effects of all three variables. These findings underscore the importance of adaptive institutions, risk-aware policymaking, and innovation-driven strategies for advancing environmental sustainability.

US-China geopolitical tensions and Indian stock market dynamics: evidence from NARDL and wavelet coherence

Exploring the optimal spatial scales of urban thermal environment factors based on wavelet coherence across full transects

Asymmetric relationship between official foreign exchange reserves, exchange rate, and stock price (BSE Sensex): Evidence from India through a wavelet coherence approach