Multivariate and scale-dependent controls of deep soil carbon after afforestation in a typical loess-covered region.

Afforestation is beneficial to improving soil carbon pools. However, due to the lack of deep databases, the variations in soil carbon and the combined effects of multiple factors after afforestation have yet to be adequately explored in >1 m deep soils, especially in areas with deep-rooted plants and thick vadose zones. This study examined the multivariate controls of soil organic carbon (SOC) and inorganic carbon (SIC) in 0-18 m deep under farmland, grassland, willow, and poplar in loess deposits. The novelty of this study is that the factors concurrently affecting deep soil carbon were investigated by multiwavelet coherence and structural equation models. On average, the SOC density (53.1 ± 5.0 kg m-2) was only 12% of SIC density (425.4 ± 13.8 kg m-2), with depth-dependent variations under different land use types. In the soil profiles, the variations in SOC were more obvious in the 0-6 m layer, while SIC variations were mainly observed in the 6-12 m layer. Compared with farmland (SOC: 17.0 kg m-2; SIC: 122.9 kg m-2), the plantation of deciduous poplar (SOC: 28.5 kg m-2; SIC: 144.2 kg m-2) increased the SOC and SIC density within the 0-6 m layer (p < 0.05), but grassland and evergreen willow impacted SOC and SIC density insignificantly. The wavelet coherence analysis showed that, at the large scale (>4 m), SOC and SIC intensities were affected by total nitrogen-magnetic susceptibility and magnetic susceptibility-water content, respectively. The structural equation model further identified that SOC density was directly controlled by total nitrogen (path coefficient = 0.64) and indirectly affected by magnetic susceptibility (path coefficient = 0.36). Further, SOC stimulated the SIC deposition by improving water conservation and electrical conductivity. This study provides new insights into afforestation-induced deep carbon cycles, which have crucial implications for forest management and enhancing ecosystem sustainability in arid regions.

The road to decarbonization in Australia. A Morlet wavelet approach.

The path toward sustainable development is closely related to the intensification of renewable energy sources and the continual innovation of technologies. To evaluate the role of renewable energy consumption and technological innovations on carbon emissions in Australia, this study uses the Morlet wavelet approach. This study identified temporal and frequency variations by applying wavelet correlation, continuous wavelet transforms, and partial and multiple wavelet coherence methods on data from 2000 to 2021. The wavelet correlation revealed that non-renewable energy, globalization, and economic growth are positively correlated with carbon emissions at all scales. In contrast, carbon emissions are negatively correlated with renewable energy and technological innovation at all scales. Meanwhile, the wavelet coherence analysis shows that non-renewable energy contributes to increased CO2 emissions from the short to long term, whereas renewable energy usage negatively affects CO2 emissions across all frequency scales. The study findings indicate that increasing the proportion of renewable energy usage in the total energy mix will curb CO2 emissions over the long run. Accordingly, the way to achieve sustainable development is shifting to a low-carbon economy centered on renewable energy sources, enhancing energy efficiency, and using carbon storage and capture technologies.

Asymmetric impacts of climate policy uncertainty, investor sentiment on energy prices and renewable energy consumption: Evidence from NARDL and wavelet coherence.

This paper seeks to look into the asymmetric impacts posed by climate policy uncertainty (CPU) and investor sentiment (IS) upon the price of non-renewable energy, specifically natural gas prices, and the consumption of renewable energy, embodied in geothermal energy, biofuels, and fuel ethanol. To this end, the analysis draws on a non-linear autoregressive distributed lag (NARDL) model and wavelet coherence (WTC) technique with monthly data from January 2000 to December 2021. The NARDL results establish an asymmetric association between the variables, where negative shocks to CPU exert a greater effect on each energy variable than positive shocks, while the reverse is true for IS. Furthermore, it has been noticed that CPU and IS exhibit primarily negative correlations with the target variables over the long term, with CPU having a more pronounced effect on natural gas prices than on other forms of renewable energy consumption. Wavelet analysis also reveals that CPU leads the energy variables over the medium to long run, while IS assumes a dominant role in the short to medium run. These momentous findings underscore the importance of this study in informing energy policy formulation and environmental management, as well as optimizing investor portfolios.

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002-2019) over Pakistan using ARIMAX model and cross-wavelet analysis.

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002-2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2-8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Influencing factors of PM2.5 concentration in the typical urban agglomerations in China based on wavelet perspective.

PM2.5 is one of the most harmful air pollutants affecting sustainable economic and social development in China. The analysis of influencing factors affecting PM2.5 concentration is significant for the improvement of air quality. In this study, three typical urban agglomerations in China (Beijing‒Tianjin‒Hebei [BTH], the Yangtze River Delta [YRD], and the Pearl River Delta [PRD]) were studied using innovative trend analysis, a Bayesian statistical model, and partial wavelet and multiwavelet coherence to analyze PM2.5 concentration variations and multi-scale coupled oscillations between PM2.5 concentration and air pollutants/meteorological factors. The results showed that: (1) PM2.5 concentration time-series showed significant downward trends, which decreased as follows: BTH > YRD > PRD. The higher the pollution level, the greater the change trend. In BTH and the PRD, PM2.5 had obvious trends and seasonal change points; whereas, the PM2.5 time-series change point in the YRD was not obvious. (2) PM2.5 had significant intermittent resonance cycles with air pollutants and meteorological factors in different time domains. There were differences in the main controlling factors affecting PM2.5 among the three urban agglomerations. (3) The explanatory ability of air pollutant combinations for variations in PM2.5 was higher than that of meteorological factor combinations. However, the synergistic effect of air pollutants/meteorological factors could better explain the PM2.5 concentration variations on all time-frequency scales. The results of this study provide a reference for ecological improvement as well as collaborative governance of air pollution.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

Investigating the nonlinear and non-stationary relationship between PM2.5 and air pollutants by wavelet signal analysis in central Taiwan.

In recent years, PM2.5 has become a critical factor as an environmental indicator, causing severe air pollution that has negatively impacted nature and human health. This study used hourly data gathered in central Taiwan from 2015 to 2019 and applied spatiotemporal data analysis and wavelet analysis methods to investigate the cross-correlation between PM2.5 and other air pollutants. Furthermore, it explored the correlation differences between adjacent stations after excluding major environmental factors such as climate and terrain. Wavelet coherence shows that PM2.5 and air pollutants mostly have a significant correlation at the half-day and one-day frequencies, while the differences between PM2.5 and PM10 are only particle size; hence, not only is the correlation the most consistent among all air pollutants but also the lag time is the most negligible. Carbon monoxide (CO) is the primary source pollutant of PM2.5 as it is also significantly correlated with PM2.5 at most timescales. Sulfur dioxide (SO2) and nitrogen oxide (NOx) are related to the generation of secondary aerosols, which are important components of PM2.5; therefore, the consistency of significant correlations improves as the timescale increases and the lag time becomes amplified. The pollution source mechanism of ozone (O3) and PM2.5 is not identical, so the correlation is lower than for other air pollutants; the lag time is also obviously influenced by the season changes that have significant fluctuations. At stations near the ocean such as Xianxi station and Shulu station, PM2.5 and PM10 have a higher correlation in the 24-h frequency, while the SO2 and PM2.5 at Sanyi station and Fengyuan station, which are close to industrial areas, have significant correlations in the 24-h frequency. This study hopes to help better understand the impact mechanisms behind different pollutants, and thus construct a better reference for establishing a complete air pollution prediction model in the future.

COVID-19 and risk spillovers of China's major financial markets: Evidence from time-varying variance decomposition and wavelet coherence analysis.

COVID-19 has influenced financial markets drastically; however, this influence has received little attention, particularly in China. This study investigates risk spillovers across China's financial and shipping markets through dynamic spillover measures based on time-varying parameter vector autoregression and generalized forecast error variance decompositions. Stock, fund, and futures markets are identified as major risk senders, whereas other markets are identified as major risk receivers. Surprisingly, bonds, gold, and shipping are safe havens that facilitate portfolio optimization. Furthermore, using wavelet coherence analysis, we find that the coherence between dynamic total spillover and COVID-19 varies across time and frequency domains.

Recent analytical tools to mitigate carbon-based pollution: New insights by using wavelet coherence for a sustainable environment.

Anthropogenic activities are a ubiquitous source of carbon-based pollution. A sustainable environment is endangered due to inefficiently regulated pollution policies, and the rise in world population further enhances the severity of this formidable challenge. This study has investigated the impact of the annual CO2 emissions in Pakistan on electricity production from different sectors, GDP, and the population by focusing on the control of carbon-based pollution. This research study intends to fill the gap in previous studies by providing significant measures to link the control of carbon-based pollution, increased GDP, and Pakistan's population, using data from 1990 to 2020. A set of 15 variables are mainly used to investigate all of these relations. Carbon pollution drastically impacts both the external and internal environment. The graphical analysis undertaken in this study finds an upward trend and significant positive correlation among the variables. It demonstrates that Pakistan shows minimal contribution in CO2 emission compared to other Asian economies, but in recent decades, an increasing growth rate has been noticeable and needs to be controlled. The ECM and ARDL approaches confirm that all the variables positively affect CO2 emission both in the long- and short-term, except for electricity production from gas and hydro in the long term, which shows negative relation. The long-term shifts also indicate that high CO2 emissions can be recovered from by adjusting these variables. The study also suggests that the government should convert high carbon use to low carbon energy use to control CO2 emissions in Pakistan.

A wavelet coherence approach to detecting ecosystem services trade-off response to land use change.

Clarifying the complex land use impacts on ecosystem services (ESs) trade-off will be beneficial to watershed sustainable development, especially through scientific land use management and decision making. Dongting Lake Basin (DLB) is not only one of the most significant ecological barriers for the Yangtze River Economic Belt, but also an important grain production base of China. The trade-off between the grain production (GP) and water purification (WP) has become increasingly prominent. Here, we chose DLB as a case study area, applied spatial continuous wavelet transform and wavelet coherence analysis, characterized the ES trade-off intensity by wavelet coherence coefficient, and explored the influence of land use type, conflict and intensity on the trade-off between GP and WP. The results showed that the trade-off intensity between GP and WP in the DLB in 2015 had alleviated compared with 2005, and the coherence coefficient had increased while maitaining the negative value. The trade-off intensity was the strongest in farmland and forest land, and weaker in grassland and water body. The impact of land use conflict mainly depended on the specific types of land use conversions. For the transects where land use conversions mainly appeared between farmland and forest land, the intensification of land use conflict would increase the trade-off intensity (2005: R2 = 0.3862, p < 0.05; 2015: R2 = 0.2543, p < 0.05), while for the transects dominated by conversions to water body and grassland, stronger land use conflicts would reduce the trade-off intensity (2005: R2 = 0.3438, p < 0.05; 2015: R2 = 0.2668, p < 0.05). The impact of land use intensity was also realized through the land use type, with lower interpretation ratio. In addition, the wavelet coherence analysis showed that the scale about 10.51 km was the most suitable for exploring the trade-off between GP and WP, which was equivalent to the scale of the secondary watershed in the study area.

Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis.

The increase in drought frequency in recent years is considered as an important factor affecting vegetation diversity. Understanding the responses of vegetation dynamics to drought is helpful to reveal the behavioral mechanisms of terrestrial ecosystems and propose effective drought control measures. In this study, long time series of Normalized Difference Vegetation Index (NDVI) and Solar-induced chlorophyll fluorescence (SIF) were used to analyze the vegetation dynamics in the Pearl River Basin (PRB). The relationship between vegetation and meteorological drought was evaluated, and the corresponding differences among different vegetation types were revealed. Based on an improved partial wavelet coherence (PWC) analysis, the influences of teleconnection factors (i.e., large-scale climate patterns and solar activity) on the response relationship between meteorological drought and vegetation were quantitatively analyzed to determine the roles of factors. The results indicate that (a) vegetation in the PRB showed an increasing trend from 2001 to 2019, and the SIF increased more than that of NDVI; (b) the vegetation response time (VRT) based on NDVI (VRTN) was typically 4-6 months, while the VRT based on SIF (VRTS) was typically 2-4 months. The VRT was shortest in the woody savannas and longest in the evergreen broadleaf forests. (c) The relationship between the SIF and meteorological drought was more significant than that between the NDVI and meteorological drought. (d) There was a significant positive correlation between meteorological drought and vegetation in the period of 8-20 years. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and sunspots were important driving factors affecting the response relationship between drought and vegetation. Specifically, the PDO had the greatest impacts among these factors.

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

The price jump behavior may bring tremendous challenges on risk management and asset pricing. This paper uses the BN-S test, the wavelet coherence method, and applies high-frequency data to explore whether and to what extent the COVID-19 pandemic impacts China's energy stock market jumps and its characteristics. The empirical results uncover the significant and heterogeneous interactions between the COVID-19 pandemic and China's energy stock market jumps across market specifications, investment horizons, and China/global pandemic tolls at different time scales. First, the oil stock market jumps were the most correlated with the pandemic, especially during the peak and re-deterioration phases. The pandemic played a positive and leading role in the short term (1-4 days length period) and long term (over 32 days length period). Second, the coal stock market jumps have similar characteristics to those of oil, but mainly show a negative correlation with the pandemic. Third, renewable energy stock market jumps were the least correlated, mainly showing a positive correlation in the short term and a negative correlation in the long term. In addition, the interaction characteristics of systemic co-jumps in different China's energy stock markets are also significant.

Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis.

The purpose of this study was to obtain synergistic information and details in the time-frequency domain of the relationships between the Palmer drought indices in the upper and middle Danube River basin and the discharge (Q) in the lower basin. Four indices were considered: the Palmer drought severity index (PDSI), Palmer hydrological drought index (PHDI), weighted PDSI (WPLM) and Palmer Z-index (ZIND). These indices were quantified through the first principal component (PC1) analysis of empirical orthogonal function (EOF) decomposition, which was obtained from hydro-meteorological parameters at 15 stations located along the Danube River basin. The influences of these indices on the Danube discharge were tested, both simultaneously and with certain lags, via linear and nonlinear methods applying the elements of information theory. Linear connections were generally obtained for synchronous links in the same season, and nonlinear ones for the predictors considered with certain lags (in advance) compared to the discharge predictand. The redundancy-synergy index was also considered to eliminate redundant predictors. Few cases were obtained in which all four predictors could be considered together to establish a significant information base for the discharge evolution. In the fall season, nonstationarity was tested through wavelet analysis applied for the multivariate case, using partial wavelet coherence (pwc). The results differed, depending on the predictor kept in pwc, and on those excluded.

Effect of orthostasis on the regulation of skin blood flow in upper and lower extremities in human.

OBJECTIVE: The research is aimed to investigate interactions between cardiovascular signals and to assess contributions of central and local mechanisms to skin blood flow regulation in upper and lower extremities at rest and under orthostasis. METHODS: Heart rate variability, respiration, forearm, and foot skin blood flow were assessed at rest and during postural test in 25 healthy volunteers. Spectral analysis was performed. Phase synchronization degree of analyzed signals was determined by group phase wavelet coherence function. RESULTS: Skin blood flow was lower on foot at rest and during postural test than on forearm. High-frequency component of heart rate variability was higher at ~0.3 Hz during postural test versus rest. Blood flow oscillation amplitudes on the foot were lower in frequency range including respiratory interval at rest than on forearm. Postural exposure increased amplitude of foot blood flow oscillations in respiratory interval and decreased amplitudes in cardiac interval versus rest. Orthostasis increased group wavelet phase coherence between foot blood flow and heart rate variability or respiration, as well as between forearm and foot blood flow at 0.3 Hz corresponding to respiration. CONCLUSIONS: The contribution of central mechanisms associated with respiration to blood flow regulation increased in lower extremities during orthostasis.

Global hotspots of coherent marine fishery catches.

Although different fisheries can be tightly linked to each other by human and ecosystem processes, they are often managed independently. Synchronous fluctuations among fish populations or fishery catches can destabilize ecosystems and economies, respectively, but the degree of synchrony around the world remains unclear. We analyzed 1,092 marine fisheries catch time series over 60 yr to test for the presence of coherence, a form of synchrony that allows for phase-lagged relationships. We found that nearly every fishery was coherent with at least one other fishery catch time series globally and that coherence was strongest in the northeast Atlantic, western central Pacific, and eastern Indian Ocean. Analysis of fish biomass and fishing mortality time series from these hotspots revealed that coherence in biomass or fishing mortality were both possible, though biomass coherence was more common. Most of these relationships were synchronous with no time lags, and across catches in all regions, synchrony was a better predictor of regional catch portfolio effects than catch diversity. Regions with higher synchrony had lower stability in aggregate fishery catches, which can have negative consequences for food security and economic wealth.

Is the COVID-19 vaccine effective on the US financial market?

OBJECTIVES: COVID-19 is the most devastating pandemic that affected humanity and the world economy. This paper aimed to study the time-varying connectedness between the COVID-19 vaccination, infection rate (INFR), and the case fatality ratio (CFR) in the United States and the stock market returns. STUDY DESIGN: We used COVID-19 daily confirmed number of infections, deaths, and vaccinations and the daily US stock market index return. METHODS: A wavelet coherence approach was used to assess the co-movement of the US stock market with the COVID-19 vaccination, INFR, and the CFR. RESULTS: The COVID-19 vaccination, INFR, and CFR have a positive and significant influence on S&P 500 returns at the majority of business cycle frequencies with an in-phase relation. CONCLUSIONS: The wavelet coherence analysis uncovers strong and significant connectedness between COVID-19 vaccination rate and S&P 500 return. From an economic perspective, the US government should continue its intervention with their vaccination strategy, as it is beneficial for fighting the pandemic. This may lead to the recovery of the stock market as well as to the whole economy.

A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking.

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes' movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes' movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes' movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.

Revisiting the sustainable versus conventional investment dilemma in COVID-19 times.

Sustainable living has emerged as the need of the hour for mankind in present times. Practitioners, as well as scholarship in the area, are divided over the comparison of financial returns from sustainable indexes vis-à-vis conventional indexes, causing investors' dilemma. These questions loom larger during the times of global crises, such as COVID-19, which have brought sustainability concerns to the limelight. This dilemma of the investors leads us to approach the study on hand. We study the Thomson Reuters/S-Network global indexes (as a proxy for sustainability-based indexes), and their corresponding alternatives, using the daily closing prices from 1st January 2011 to 29th June 2020. We apply the time-frequency-based Granger-Causality test, and further attempt to understand the coherence between these indexes before and during the COVID-19 period by using the Wavelet Coherence and phase-difference mechanisms. Our results suggest short-run uni-directional causality from sustainable indexes to conventional indexes whereas bi-directional causality in medium and the long-runs. The coherence is particularly stronger at low frequencies, indicating the long-run coherence with sustainable indexes in the lead during COVID-19. The results and conclusions of the study have important implications for different audiences. The portfolio and fund managers can prefer to invest in such markets to avail of higher returns over a longer period.

Time-frequency co-movement between COVID-19, crude oil prices, and atmospheric CO2 emissions: Fresh global insights from partial and multiple coherence approach.

This paper endeavors to analyze and provide fresh global insights from the asymmetric nexus between the recent outbreak of COVID-19, crude oil prices, and atmospheric CO2 emissions. The analysis employs a unique Morlet's wavelet method. More precisely, this paper implements comprehensive wavelet coherence analysis tools, including continuous wavelet coherence, partial wavelet coherence, and multiple wavelet coherence to the daily dataset spanning from December 31, 2019 to May 31, 2020. From the frequency perspective, this paper finds significant wavelet coherence and vigorous lead and lag connections. This analysis ascertains significant movement in variables over frequency and time domain. These results demonstrate strong but varying connotations between studied variables. The results also indicate that COVID-19 impacts crude oil prices and the most contributor to the reduction in CO2 emissions during the pandemic period. This study offers practical and policy implications and endorsements for individuals, environmental experts, and investors.

The unprecedented reaction of equity and commodity markets to COVID-19.

Using a drifting spillover index approach (Diebold and Yilmaz, 2012) and a continuous time-frequency tool (Torrence and Webster, 1999), this paper attempts an empirical investigation of the spillovers and co-movements among commodity and stock prices in the major oil-producing and consuming countries. While our results point to the existence of a significant interdependence among the markets considered, Chinese and Saudi Arabian stock markets seem to be weakly integrated into the world market. Moreover, the spillovers are time-varying and reached their highest levels during the COVID-19 medical shock.