Monthly Dataset Research Articles

Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Abstract. The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO2 (pCO2), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface pCO2 datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface pCO2 in the SCS with a reasonably high spatial resolution (0.05∘ × 0.05∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of pCO2 constrained by EOF, atmospheric pCO2, and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed pCO2 field successfully simulated the spatial and temporal patterns of sea surface pCO2 observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm, which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed pCO2 and in situ data ranged from −10 to 4 µatm (−2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface pCO2 distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface pCO2 in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface pCO2 in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO2, the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at https://doi.org/10.57760/sciencedb.02050 (Wang and Dai, 2022).

Evaluation of US and China's post-pandemic recovery

We focus on the relationship between inflation and unemployment in the United States and China, the two biggest economic systems in the world before and after the COVID-19 pandemic. We used a comprehensive monthly dataset of two countries Consumer Price Index (CPI), unemployment rate, and interest rate from 2019 to 2022 and performed time series regressions to analyze the relations among these variables. We found strikingly different paths for the two countries, with the US path closely matching conventional economic theory, while Chinas deviated significantly.

An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

한류가 우리나라 수출입에 미친 상호 영향에 관한 실증적 연구

Purpose – This study is an empirical study on the relationship among export volume and import volume, with BTS internet search trends indexed. Design/Methodology/Approach – In this study, we used 111 monthly data sets on BTS internet search trends, the export volume, and the import volume from April 30, 2013 to Jun 30, 2022. We try to analyze the mutual influence and causality among BTS internet search trends, the export volume, and the import volume. We seek to analyze the extent of any cross-influence. We employ a variance decomposition function based on a VAR model, as well as impulse response after a cointegration test and unit root test. Findings – The important results of this study are summarized as follows. First, there is at least one cointegration among the first differential data of BTS internet search trends, export volume, and import volume. Secondly, import volume does have Granger causality with BTS internet search trends. The export and import volume show Granger causality. BTS internet search trends and the export volume have Granger causality with BTS internet search trends and export volume. Research Implications – This study differs in that it uses BTS internet search trends and the Korean wave. It has limitations in that the study period is short and the study sample is limited.

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

China is one of the regions with the most frequent drought disasters and serious social and economic losses. Agricultural drought is the most serious natural disaster. Due to climate change, the regional agricultural drought risk assessment has always been the focus of the academic circle. This study takes Zunyi City as an example, which is the most typical city of karst landform development. The monthly precipitation data set of ground meteorological observation stations in Zunyi City from 1956 to 2020 was selected, and the drought characteristic variables were extracted by the coupled use of the precipitation anomaly percentage (Pa) index and the theory of runs. A copula function was applied to establish the joint distribution model of characteristic variables, obtaining the drought frequency and drought return periods. Combined with the Jensen model, the agricultural drought loss rate under different drought return periods in the target year (2020) was calculated and evaluated. The results showed that the Gumbel-Hougaard copula function was suitable for the joint distribution of drought joint variables in Zunyi City. From 1956 to 2020, fewer droughts occurred in Zhengan and Wuchuan, and the most droughts took place in Fenggang, Meitan, and Yuqing. The average drought duration in each county was about 1.5 months, and the average drought severity was about 0.35 in spatial distribution. Crop loss rate caused by drought increased and the affected area expanded with the increase of drought return periods (5, 10, 20, 50, and 100 years) in temporal distribution. Meanwhile, the drought disaster was most drastic in the eastern region, followed by the south, north, west, and central area. The results were highly consistent with the historical drought in Zunyi City, which verified the validity of the model. This study could provide scientific knowledge for drought resistance and reasonable mitigation programing for the security of the regional agricultural production and the sustainability of social and economic development.

Dynamic association of economic policy uncertainty with oil, stock and gold: a wavelet-based approach

PurposeThis research unfolds a holistic association between economic policy uncertainty (EPU) and three important markets (oil, stock and gold) in the Indian context. To do same, the current study uses the monthly dataset of each variable spanning from November 2005 to March 2022.Design/methodology/approachThe authors have portrayed the wavelet-based coherence, correlation and covariance plots to explore the interaction between EPU and markets' behavior. Then, a wavelet-based quantile on quantile regression model and wavelet-based Granger causality has been applied to examine the cause-and-effect relation and causality between the EPU and markets.FindingsThe authors’ findings report that the Indian crude oil buyers do not need to consider Indian EPU while negotiating the oil deals in the short term and medium term. However, in case of the long-term persistence of uncertainty, it becomes difficult for a buyer to negotiate oil deals at cheap rates. EPU causes unfavorable fluctuation in the stock market because macroeconomic decisions have a substantial impact on it. The authors have also found that gold is a gauge for economic imbalances and an accurate observer of inflation resulting from uncertainty, showing a safe haven attribute.Originality/valueThe authors’ work is original in two aspects. First, their study solely focused on the Indian economy to investigate the impact and causal power of Indian EPU on three major components of the Indian economy: oil, stock and gold. Second, they will provide their findings after analyzing data at a very microlevel using a wavelet-based quantile on quantile and wavelet-based Granger causality.

A parent-school initiative to assess and predict air quality around a heavily trafficked school

Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM1, PM2.5, PM10), gaseous pollutants (NO2, O3 and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021, owing to new year celebration fireworks, when the daily average PM2.5 was around three-times the World Health Organisation limit. PM2.5 and NO2 exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM2.5 and NO2, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO2 dataset that performed moderately (R2, 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.

Impact of Brexit policy on textile/clothing and footwear UK imports

On 23 June 2016 the UK voted to leave the EU, (Brexit). A four year and six-month period of uncertainty ensued before Article 50 was activated. Eventually the UK-EU Trade and Cooperation Agreement (TCA) came into force on 1 May 2021. The UK retail sector, in particular the textile/clothing and footwear, relies on high levels of international importation to the UK. As the negotiations between the UK and the EU were undertaken, key elements featured in the development of the new Brexit policy. This paper examines the impact on the textile/clothing and footwear UK importation trade in relation to three of these key elements:- • June 23, 2016, UK voted to leave the EU. • 29 March 2017 Article 50 was triggered. • October 2019 Article 50 extended. The data was modelled using the main five UK import partners in relation to textile/clothing and footwear from the WITS (World Integrated Trade Solution) database and the UK Office of National Statistics annual and monthly data sets, in relation to the three key elements of policy. The study provided an initial insight into the impact of policy decision making during the Brexit process. The findings show that Key elements of Brexit policy making had a negative impact on importation trade in relation to Textile/Clothing and Footwear. Further research is recommended to establish the long-term effects of Brexit policy on the UK retail economy as a whole, over the next five years. This to be extended to the export of textile/clothing and footwear from the UK to EU and Non-EU trading partners.

Temporal Variability of Temperature, Precipitation and Drought Indices in Hyper-Arid Region of Northwest China for the Past 60 Years

The temporal variability and abrupt change analysis of temperature and precipitation in Turpan was investigated and examined based on a monthly data set of temperature, precipitation and drought indices (1960–2019) from three meteorological stations over the study area. The Yamamoto method, Mann–Kendall test, Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), and Reconnaissance Drought Index (RDI) were applied to reveal temperature, precipitation and drought indices trends in their annual volumes. The conclusions were as follows: (1) in the past 60 years, the annual average temperature in the Turpan region has increased at a rate of 0.33 °C·(10a)−1. Whereas the temperature has accelerated since the 1980s, the annual average minimum temperature has increased more than the annual average maximum temperature, and the temperature difference between winter and summer has increased since the 1990s. (2) The inter-annual, inter-decadal and normal value changes in precipitation in the Turpan region and its three meteorological stations indicated a decreasing trend during 1960–2019. Though the normal value of summer and autumn precipitation decreased and increased as a whole, the normal values of spring, summer, winter and annual precipitation in the Turpan region displayed downward trends. (3) Abrupt changes in temperature were observed in the mid-1990s, and abrupt changes in precipitation were not obvious. (4) The SPI and RDI responded quickly to precipitation and temperature, and the overall characteristics of dry and wet trend changes were consistent. When the SPEI considered the combined effect of temperature and precipitation, the SPI and SPEI are better correlated, and the SPI and RDI are better correlated than the SPEI and RDI. On the whole, the occurrence of drought has obvious regional and seasonal characteristics. These conclusions not only provide scientific data for sustainable development in Turpan but also offer scientific information to further understand the trends and periodicity of climate change and drought conditions in hyper-arid regions around the world.

Do People Smooth their After-Tax Income? Evidence from Japanese Local Tax

Abstract This study provides evidence that under the Japanese local individual income tax system, individuals smooth their after-tax income by choosing the timing of their tax payments. We construct a monthly data set of Japanese local taxes with sample periods for over 26 years. The results show that though the tax amounts are pre-determined in one-year units by the system, individuals pay more taxes during months when their incomes are high, such as in “bonus” periods, than other months in a year. The t-statistics for means indicates that there exist significant upward deviations during these months. JEL classification numbers: H31; H24; E62. Keywords: Consumption smoothing; Local income tax; Inter-temporal decision making; Mean test; Levene test.

Impact of Brexit policy on textile/clothing and footwear UK imports

On 23 June 2016, the UK voted to leave the EU, (Brexit). A four year and six-month period of uncertainty ensued before Article 50 was activated. Eventually the UK-EU Trade and Cooperation Agreement (TCA) came into force on 1 May 2021. The UK retail sector, in particular the textile/clothing and footwear, relies on high levels of international importation to the UK. As the negotiations between the UK and the EU were undertaken, key elements featured in the development of the new Brexit policy. This paper examines the impact on the textile/clothing and footwear UK importation trade in relation to three of these key elements:- June 23, 2016, UK voted to leave the EU. 29 March 2017 Article 50 was triggered. October 2019 Article 50 extended. The data was modelled using the main five UK import partners in relation to textile/clothing and footwear from the WITS (World Integrated Trade Solution) database and the UK Office of National Statistics annual and monthly data sets, in relation to the three key elements of policy. The study provided an initial insight into the impact of policy decision making during the Brexit process. The findings show that Key elements of Brexit policy making had a negative impact on importation trade in relation to Textile/Clothing and Footwear. Further research is recommended to establish the long-term effects of Brexit policy on the UK retail economy as a whole, over the next five years. This to be extended to the export of textile/clothing and footwear from the UK to EU and Non-EU trading partners.

Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model

Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy.

Analysis of a comprehensive monthly dataset on nitrogen, phosphorus and organic carbon in the Venice lagoon waters (Italy)

Analysis of a comprehensive monthly dataset on nitrogen, phosphorus and organic carbon in the Venice lagoon waters (Italy)

Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States

The impact of wildfire on water availability is a critical issue in the western United States. Because actual evapotranspiration (ETa) constitutes the largest loss in the terrestrial water budget, it has been suggested that fire-induced ETa reduction is a primary driver of elevated post-fire discharge. Ten gaged watersheds with burns exceeding 5% of their total contributing drainage area were selected from California, Oregon, Montana, Utah, New Mexico, and Colorado. Continuous daily stream gage data were compiled, and 30-meter ETa estimates were calculated with the Operational Simplified Surface Energy Balance (SSEBop) model. Fire-induced ETa shifts were quantified with statistical tests that compared pre and post-fire monthly ETa in burned and unburned pixels; the dampening effect of scale was also evaluated by repeating tests on all pixels from the entire basin. As streamflow data are point measurements that aggregate a large spatial area, additional statistical methods were required to isolate the effect of fire from climate on baseflow and runoff. Key findings include a) significant fire-induced ETa reductions were only distinguishable in basin-scale monthly datasets when at least 73% of the basin burned, b) the effect of wildfire disturbance on streamflow magnitude was seasonably variable, c) streamflow was modified in basins with as little as 6% burned drainage area; however, shifts only persisted beyond the fifth post-fire year where more than three-quarters of the basin was fire-impacted, and d) surplus water from ETa reduction was sufficient to account for boosted fire-induced streamflow. Where fire-induced streamflow increases were not significantly correlated with ETa anomaly, other fire-impacted landscape processes may have contributed to modified runoff generation and routing. Where fire reduced ETa but streamflow shifts were not detected, compensatory ETa pathways may have consumed the excess water before it reached the gage. Findings suggest that water providers with small source-water collection areas have higher relative risk for fire-induced hydromodification than providers with larger or more diversified supply portfolios. Results also illustrate the tendency of overarching climate signals to mask or artificially boost the apparent effect of landscape disturbance on streamflow at the basin outlet.

Evidence of long term biogeochemical interactions in carbonate weathering: The role of planktonic microorganisms and riverine bivalves in a large fluviokarst system

The infiltration of organic-rich surface waters towards groundwaters, is known to play a significant role in carbonate weathering and in contributing to the atmospheric continental carbon sink. This paper investigated biogeochemical interactions in karst critical zones, with strong surface water /groundwater interactions, and in particular the role of planktonic microorganisms and riverine bivalves through the analysis of particulate organic matter (OM) oxidation on carbonate weathering. In the large Val d'Orléans fluviokarst aquifer (France), a 20-year monthly dataset of Nitrates, Dissolved Oxygen (DO), dissolved inorganic and organic Carbon (DIC and DOC) fluxes was gathered. The surface water-groundwater comparison of geochemical trends showed that planktonic microorganisms had drastically decreased in surface waters, related to the proliferation of Corbicula bivalves spreading and a decrease in nutrients. This decrease in planktonic microorganisms was followed by a DO increase and an DIC decrease at the karst resurgence. The degradation of planktonic microorganisms consumes DO and produces NO3, dissolved inorganic carbon (DIC) and a proton that in turn, dissolves calcite and produces DIC. Without the input from planktonic microorganisms, the fluviokarst has lost 29 % of this nitrification and 12 % of the carbonate dissolution capacities. Thus, the oxidation of particulate organic matter of planktonic microorganisms, which is part of heterotrophic respiration, appears to be a significant source of the inorganic carbon flux in riverine ecosystems. This shows how weathering can remain active under waters saturated versus calcite and suggests that the oxidation of organic matter can be a more appropriate mechanism than autotrophic respiration to explain the relationship between global warming and DIC flux change in rivers. Through the consumption of plankton, the animal life in rivers thus influences the inorganic carbon in groundwaters, creating a negative feedback in the carbon cycle.

An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction

Accurate prediction of wind speed is of great significance to the stable operation of wind power equipment. In this study, a hybrid deep learning model based on convolutional neural network (CNN), Bi-directional long short-term memory (BiLSTM), improved sine cosine algorithm (ISCA) and time-varying filter based empirical mode decomposition (TVFEMD) is proposed for wind speed prediction. Firstly, the original wind speed data is decomposed into intrinsic mode functions (IMFs) by TVFEMD to improve the data stability. Then, the importance of each decomposed subcomponent is analyzed using random forest (RF). Thirdly, CNN-BiLSTM is employed to predict the wind speed. And, an improved sine and cosine algorithm (ISCA) is utilized to optimize the model parameters BiLSTM. Finally, the forecasting results of each sub-model are combined to get the final prediction results. In this study, the proposed model is utilized to four monthly wind speed data sets, and different comparison models are established. The experimental results of this study show that TVFEMD and RF can process data more effectively and improve the prediction accuracy. ISCA can optimize the parameters of BiLSTM model and improve the prediction performance. The proposed model in this study can obtain good prediction results on all data sets.

Weighted Subsethood Fuzzy Time Series towards Energy-Water Efficiency for Water Treatment Plant

Forecasting is the process that uses statistical tools to make predictions based on past and present data. In the water treatment plant systems, forecasting is useful to increase the efficiency of energy-water units by forecasting the amount of energy used in the current and future time. Fuzzy time-series (FTS) employs an algorithm or statistical model to predict the future condition. By analysing the patterns of data, fuzzy time-series forecasting can forecast the future condition of a system. The FTS approaches have been increasingly popular in recent years, as there are so many methods available for a researcher to apply to forecast the future based on the needs of the user. This paper proposed the modification of the classical fuzzy time series by applying weighted subsethood Fuzzy Time Series (WeSuFTS) in the water management industry. 2 sets of monthly data, which are water production and energy consumption data from January 2017 to December 2020, are used in this study. To measure the performance of the proposed model, the forecast results of the proposed model were compared with the forecast results of Chen and Cheng models. This study compares 3 different models using Absolute Percentage Error (APE) and Mean Absolute Percentage Error (MAPE). The WeSuFTS model delivers a good performance in the evaluation part with MAPE 0.9252% and APE value between 0.02226% and 1.7877%.

ON THE RELATIONSHIP BETWEEN ECONOMIC POLICY UNCERTAINTY, GEOPOLITICAL RISK AND STOCK MARKET RETURNS IN SOUTH KOREA: A QUANTILE CAUSALITY ANALYSIS

In this research, we assess the influence of geopolitical risk (GPR), exchange rate (EXCH) and economic policy uncertainty (EPU) on South Korea stock market. Using monthly dataset covering the period from 1997 to 2021, we utilized the novel non-parametric causality-in-quantiles test initiated by Balcilar et al. (2017) to assess these associations. This study discovered that the effect of macroeconomic shocks in South Korea is diverse across the stock market, implying that the influence of these shocks is not consistent across the stock market in South Korea. Furthermore, this study revealed that the causal influence of EPU, GPR on the stock market is visible in mean and variance. Nonetheless, the causal influence of EXCH on the stock market can only be seen in the mean, with no indication of causation in the variance. We consider that these results are significant to South Korean stock market investors for the purposes of foreign portfolio diversification and establishing investing strategies during times of economic uncertainty.

At odds? How European governments decided on public health restrictions during COVID-19

ObjectivesThis study aimed to understand how politics, economics, and public health restrictions affected each other during the COVID-19 pandemic. MethodsWe use seemingly unrelated regressions on a monthly data set of government approval ratings, the stringency index, the time-dependent reproduction number (R), and unemployment, allowing the residuals in each regression to be correlated with each other. We also conduct sensitivity tests using weekly data and the growth in polls. ResultsThe study covers 27 European countries from April 2020 to April 2021. A unit increase in the R and COVID-19 cases per million increases the stringency index by 23.742 and 4.207, respectively; a unit increase in stringency boosts the incumbent’s popularity by 0.384; the poll positively affects the stringency index; stringency has negative effects on the R; and the poll and stringency index have opposite effects on unemployment. ConclusionPolitical and economic pressures did not hinder the government from introducing stronger measures.

Natural resources commodity prices volatility and economic uncertainty: Evaluating the role of oil and gas rents in COVID-19

Indeed, natural resources are the most tradable goods across the globe and are responsible for running the industrial sector of each country. However, with the recent outbreak of Covid-19, the demand and supply chain of natural resources is greatly influence and causes economic recession, which needs to be addressed. The Covid-19 pandemic increases uncertainty in the global economic policy and natural resource commodity prices. Although the literature provides empirical outcomes in the pre-Covid-19 period, still the influence in the Covid-19 remained unexplored and needs further research. In this regard, the current study examined the US natural resources commodity price volatility and global economic uncertainty while utilizing the monthly dataset from November 01, 2007, to May 01, 2021. Using the wavelet power spectrum and wavelet coherence approaches, the empirical findings of the wavelet power spectrum reveal volatilities in the global economic policy uncertainty and crude oil price in the short run. In contrast, the oil rent, natural gas rents and total natural resources show long-run vulnerabilities. Additionally, the wavelet coherence unveils bidirectional causal association global economic policy uncertainty and other variables. However, the causal influence of global economic performance is found higher than the others. Besides, the crude oil prices and oil rents are associated with global economic policy uncertainty after the Covid-19 pandemic. These findings could have essential economic and energy implications for governors, policymakers, and researchers.