Investigating the Co-movement Nexus Between Air Quality, Temperature, and COVID-19 in California: Implications for Public Health

This research aims to look at the link between environmental pollutants and the coronavirus disease (COVID-19) outbreak in California. To illustrate the COVID-19 outbreak, weather, and environmental pollution, we used daily confirmed cases of COVID-19 patients, average daily temperature, and air quality Index, respectively. To evaluate the data from March 1 to May 24, 2020, we used continuous wavelet transform and then applied partial wavelet coherence (PWC), wavelet transform coherence (WTC), and multiple wavelet coherence (MWC). Empirical estimates disclose a significant association between these series at different time-frequency spaces. The COVID-19 outbreak in California and average daily temperature show a negative (out phase) coherence. Similarly, the air quality index and COVID-19 also show a negative association circle during the second week of the observed period. Our findings will serve as policy implications for state and health officials and regulators to combat the COVID-19 outbreak.

Assessing the Co-Movements between Electricity Use and Carbon Emissions in the GCC area: Evidence from a Wavelet Coherence Method

This paper investigates the association between CO2 emissions and a range of factors, including electricity consumption, economic growth, urbanization, and trade openness for six Gulf Cooperation Council (GCC) countries using data covering the 1965-2019 period. Namely, Oman, Saudi Arabia, the UAE, Kuwait, Bahrain, and Qatar. Contrasting with the standard literature, our empirical strategy uses the wavelet coherence approach on the frequency domain, thought to complement the time-series econometric procedures reported on this topic. Supplied at the country-level, associated evidence presents far-reaching policy recommendations whose applications may directly benefit environmental planning and bring high information value for the sake of sustainable energies in the Gulf region.JEL: Q43, C22, C23, E20, O44.

Are ESG stocks safe-haven during COVID-19?

Purpose The purpose of this study is to investigate safe-haven properties of environmental, social and governance (ESG) stocks in global and emerging ESG stock markets during the times of COVID-19 so that portfolio managers and equity market investors could decide to use ESG stocks in their portfolio hedging strategies during times of health and market crisis similar to COVID-19 pandemic. Design/methodology/approach The study uses a wavelet coherence framework on four major ESG stock indices from global and emerging stock markets, and two proxies of COVID-19 fear over the period from 5 February 2020 to 18 March 2021. Findings The results of the study show a positive co-movement of the global COVID-19 fear index (GFI) with ESG stock indices on the frequency band of 32 to 64 days, which confirms hedging and safe-haven properties of ESG stocks using the health fear proxy of COVID-19. However, the relationship between all indices and GFI is mixed and inconclusive on a frequency of 0–8 days. Further, the findings do not support the safe-haven characteristics of ESG indices using the market fear proxy (IDEMV index) of COVID-19. The robustness analysis using the CBOE VIX as a proxy of market fear supports that ESG indices do not possess safe-haven properties. The results of the study conclude that the safe-haven properties of ESG indices during the ongoing COVID-19 pandemic is contingent upon the proxy of COVID-19 fear. Practical implications The findings have important implications for the equity investors and assetty managers to improve their portfolio performance by including ESG stocks in their portfolio choice during the COVID-19 pandemic and similar health crisis. However, their investment decisions could be affected by the choice of COVID-19 proxy. Originality/value The authors believe in the originality of the paper due to following reasons. First, to the best of the knowledge, this is the first study investigating the safe-haven properties of ESG stocks. Second, the authors use both health fear (GFI) and market fear (IDEMV index) proxies of COVID-19 to compare whether safe-haven properties are characterized by health fear or market fear due to COVID-19. Finally, the authors use the wavelet coherency framework, which not only takes both time and frequency dimensions of the data into account but also remains unaffected by data stationarity and size issues.

Analysis of spatiotemporal variations of drought and its correlations with remote sensing-based indices via wavelet analysis and clustering methods

Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT–EEMD–K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satellite sensor data (i.e. the Normalized Difference Vegetation Index, the Vegetation Condition Index, the Vegetation Healthy Index, and the Temperature Condition Index) was evaluated. The possible links between central stations of clusters and satellite-based indices were assessed via the wavelet coherence method. The results revealed that all applied satellite-based indices had significant statistical correlations with the ground-based drought index within a certain period.

Tidal frequencies and quasiperiodic subsurface water level variations dominate redox dynamics in a salt marsh system

Salt marshes are hotspots of nutrient processing en route to sensitive coastal environments. While our understanding of these systems has improved over the years, we still have limited knowledge of the spatiotemporal variability of critical biogeochemical processes within salt marshes. Sea-level rise will continue to force change on salt marsh functioning, highlighting the urgency of filling this knowledge gap. Our study was conducted in a central California estuary experiencing extensive marsh drowning and relative sea-level rise, making it a model system for such an investigation. Here we instrumented three marsh positions with different degrees of inundation (6.7%, 8.9%, and 11.2% of the time for the upper, middle, and lower marsh positions, respectively), providing locations with varied geochemical characteristics and hydrological interaction at the site. We continuously monitored redox potential (Eh) at depths of 0.1, 0.3, and 0.5 m, subsurface water levels (WL), and temperature at each marsh position to understand how drivers of subsurface biogeochemical processes fluctuate across tidal cycles, using wavelet analyses to explain the interactions between Eh and WL. We found that tidal forcing significantly affects biogeochemical processes by imparting controls on Eh variability, likely driving subsurface hydro-biogeochemistry of the salt marsh. Wavelet coherence showed that the Eh-WL relationship is non-linear, and their lead-lag relationship is variable. We found that precipitation events perturb Eh at depth over timescales of hours, even though WL show relatively minimal change during events. This work highlights the importance of high-frequency measurements, such as Eh, to help explain factors that govern subsurface geochemistry and hydrological processes in salt marshes.