Time-Frequency Co-Movements Between Biomass Energy Consumption and Human Development in Brics Countries

This paper aims to investigate the influence of biomass energy consumption on human development in BRICS countries in the frequency-time domain using the wavelet frameworks. Specifically, the wavelet coherency method of Rua (2013) and the wavelet-Granger causality test of Olayeni (2016) are utilized to quantify the strength and direction of causal relationships through time and across various frequencies simultaneously. The empirical find-ings uncovered that the causal linkages between human development and biomass energy consumption in the BRICS countries are not homogeneous in different time and frequency scales. We also discover the strong relationship between the two variables in China, Russia, Brazil, and South Africa after the global financial crisis 2008 at low and medium frequencies, while this connection is somewhat low in India over the sample period. This study suggests the importance of biomass energy for human development in BRICS countries.

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.

Geopolitical-Risk and Economic Policy–Uncertainty Impacts on Tourist Flows from Neighboring Countries: A Wavelet Analysis

This paper investigates the nexus of geopolitical risks (GPRs), economic policy uncertainty (EPU), and tourist arrivals in South Korea. Specifically, this research examines whether arrivals from neighboring tourism source countries (i.e., China and Japan) are influenced by geopolitical events and economic volatilities in South Korea. To establish the research purpose, we investigated the relationships among GPRs, EPU, and tourism demand by using monthly data from January 2003 to November 2019. Additionally, innovative techniques (continuous wavelets, wavelet coherency, and wavelet phase difference) were employed, which allow the decomposition of time series considering different time and frequency components. The results demonstrate inconsistent and heterogeneous co-movements between variables that are localized across different time periods and frequencies. In addition, we detected several significant coherencies that prove the important role of GPR and EPU in explaining changes in the numbers of tourists arriving in South Korea from China and Japan. In terms of time domain, negative and positive correlations in tourism demand were detected, meaning that economic and geopolitical shocks may not always lead to negative consequences. From the frequency domain, the causal effects of GPR mostly appear to have short- to mid-run implications, with almost no relationship in the low-frequency band, whereas EPU holds a heterogeneous influence varying short-term to long-term, including higher to lower frequencies. Results show the resilience of the tourism industry against the transient effects of economic and geopolitical shocks. Tourists become adversely affected by external events such as geopolitical risks and economic uncertainties, but the impact is not consistent over time for tourists from countries neighboring Korea. The findings provide a deeper understanding of how crisis events, including political instability and economic fluctuations, can affect inbound tourism in geographically and historically interrelated countries. Therefore, to minimize the negative effect on tourism demand, it is important for practitioners to consider potential external threats when making forecasts.

Does Heterogeneity in COVID-19 News Affect Asset Market? Monte-Carlo Simulation Based Wavelet Transform

The current study investigates the connectedness between US COVID-19 news, Dowes Jones Index (DJI), green bonds, gold, and bitcoin prices for the period 22 January 2020–3 August 2021. The study has employed wavelet coherency, the continuous wavelet transform, and the wavelet-based Granger causality methods to obtain the dependence result. The continuous wavelet transform (CWT) analysis reveals that the United States equity market prices are extremely sensitive with regard to spreading coronavirus (USCOVID-19) news and changes in the oil price. Green bonds, gold, and bitcoin have minimal connectedness with the equity market, which might lead to the hedge and safe haven role of these assets during the COVID-19 crisis period. Lastly, very strong comovement was found between bitcoin and gold during the entire sample. The results of the present study offer a number of fresh and noticeable policy implications for international investors and asset managers.

Characterizing Multiscale Effects of Climatic Factors on the Temporal Variation of Vegetation in Different Climatic Regions of China

Vegetation dynamic is sensitive to climatic warming, and is affected by individual or combined climatic factors at different temporal scale with different intensity. Previous studies have unraveled the relationships between vegetation condition and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamic was dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore the scale-specific univariate and multivariate controls on vegetation over the period 1982–2015 using bivariate wavelet coherency (BWC), multiple wavelet coherence (MWC), and multiple empirical model decomposition (MEMD). The results indicated that the significant vegetation dynamics were mainly located at scales of 1, 0.5, and 0.3 years. The combined explanatory power of the seven climatic factors on the vegetation were greater at the short-term and long-term scales, while the individual climatic factor might affect vegetation dynamic in the seasonal and medium-term scales at some climatic regions. The combined effect of climatic factors in grassland of Tibetan Plateau (TP) and Tempera grassland of Inner Mongolia (TGIM) regions were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate for crop dynamics between temperate humid & subhumid Northeast China (THSNC) and TP, warm-temperate humid & subhumid North China (WHSNC) and subtropical humid Central & South China (SHCSC), and TGIM and temperate & warm-temperate desert of Northwest China (TWDNC) were equivalent, which were around 47%, 45%, and 39%, respectively. Farming practices in cropland could alleviate the spatial variation of the relationships between climate and vegetation, while enhance the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly in the medium-term scale. Collectively, the results might provide alternative perspective for understanding vegetation evolution in response to climatic changes in China.

Economy–energy markets nexus during COVID-19: A dynamic time–frequency analysis

We investigate dynamics between the economic activities and energy markets—both conventional and clean energy markets, with a sample of daily data from 1 January 2020 to 25 November 2020. We perform wavelet-based time–frequency techniques and measure the market volatility with continuous wavelet transforms. Besides, we use wavelet coherency to understand the co-movement of economic activities and energy markets and employ a nonlinear phase-difference technique to understand the time-varying causality between different series. Our continuous wavelet transform results show that all three market indices experience significant volatility in the coronavirus disease (COVID-19) period, notably during the initial period of the outbreak. The market volatilities are comparatively more substantial in the lower frequency band than the upper frequency, while the latter sustained longer in the markets. Moreover, wavelet coherency results show a strong correlation between the economic activity index and both energy market indices; however, the co-movement is significantly higher for the conventional energy market than the clean energy market. We further detect a positive and bi-directional causality between economic activities and energy market indices. Besides providing fresh and time-varying and frequency-varying relationship between global economic activities and the energy markets, which is currently lacking in the existing literature, our study has significant implications for the heterogeneous market participants in terms of improved price prediction accuracy. Furthermore, our findings can aid policymakers in decision making by showing that the dynamics between energy markets and economic activities change even within a short period, and imply that suitable constant policy interventions are necessary to avoid long-term predicament.

Time-Varying Impact of Economic Growth on Carbon Emission in BRICS Countries: New Evidence From Wavelet Analysis

Carbon emission leads to environmental and social consequences, which could be severe in the emerging economies. Owing to the dilemma of emission and economic expansion, it is necessary to achieve a more comprehensive understanding of the dynamic relationship between economic growth and carbon emission. Multivariate Wavelet analysis is introduced in addition to the decoupling analysis for BRICS countries. The decoupling analysis detects an obvious trend of economic growth decoupling from carbon emission in China, and generates mixed results for the other countries. Estimates of wavelet coherency suggest that BRICS countries have experienced different kinds of structural changes in growth–emission nexus. Results of partial phase-difference and wavelet gain imply that different resource endowments and growth paths lead to varied impact of economic growth on carbon emission and time-varying characteristics of the causality relationship over different frequencies. Energy structure and trade openness matter for anatomizing this time-varying relationship. To succeed in the fight against climate change, the policy makers need to pay serious attention to the dynamic impact of economic growth, energy structure, and trade openness on carbon emission.

Wavelet Analysis of Soil Water State Variables for Identification of Lateral Subsurface Flow: Lysimeter versus Field Data

<p>Preferential and lateral subsurface flow may be responsible for the accelerated transport of water and solutes in sloping agricultural landscapes; however, the process is difficult to observe. One idea is to compare time series of soil moisture observations in the field with those in lysimeters, where flow is vertically oriented. This study aims at identifying periods of deviations in soil water contents and pressure heads measured in the field and in a weighing lysimeter with the same soil profile. Wavelet Coherency Analysis (WCA) was applied to time series of hourly soil water content and pressure head data (15, 32, 60, 80, and 140 cm depths) from Colluvic Regosol soil profiles in summer 2017. The phase shifts and periodicities indicated by the WCA plots reflected the response times to rain events in the same depth of lysimeter and field soil. For many rain events and depths, sensors installed in the field soil showed a faster response than those in the lysimeters soil. This could be explained by either vertical preferential flow or lateral subsurface flow from upper hillslope positions. Vice versa, a faster sensor response in the lysimeter soil could be indicative for vertical preferential effects. The WCA plots comprise all temporal patterns of time shifts and correlations between larger data time series in a condensed form to identify potentially relevant periods for more detailed analyses of subsurface flow dynamics. </p>

Linking Holocene East Asian monsoon variability to solar forcing and ENSO activity: Multi-proxy evidence from a peatland in Northeastern China

High-resolution proxy-based paleoenvironmental records derived from peatlands provide important insights into climate changes over centennial to millennial timescales. In this study, we present a composite climatic index (CCI) for the Hani peatland from northeastern China, based on an innovative combination of pollen-spore, phytolith, and grain size data. We use the CCI to reconstruct variations of the East Asian summer monsoon (EASM) intensity during the Holocene. This is accomplished with complete ensemble empirical mode decomposition (CEEMD), REDFIT, and cross-wavelet coherency analysis to reveal the periodicities (frequencies) of the multi-proxy derived CCI sequences and to assess potential external forcing of the EASM. The results showed that periodicities of ca. 300–350, 475, 600, 1075, and 1875 years were present in the Hani CCI sequence. Those periodicities are consistent with previously published periodicities in East Asia, indicating they are a product of external climate controls over an extensive region, rather than random variations caused by peatland-specific factors. Cross-wavelet coherency analysis between the decomposed CCI components and past solar activity reconstructions suggests that variations of solar irradiation are most likely responsible for the cyclic characteristics at 500-year frequency. We propose a conceptual model to interpret how the sun regulates the monsoon climate via coupling with oceanic and atmospheric circulations. It seems that slight solar irradiation changes can be amplified by coupling with ENSO events, which result in a significant impact on the regional climate in the East Asian monsoon area.

Technical Note: Improved partial wavelet coherency for understanding scale-specific and localized bivariate relationships in geosciences

Bivariate wavelet coherency is a measure of correlation between two variables in the location–scale (spatial data) or time–frequency (time series) domain. It is particularly suited to geoscience, where relationships between multiple variables differ with locations (times) and/or scales (frequencies) because of the various processes involved. However, it is well-known that bivariate relationships can be misleading when both variables are dependent on other variables. Partial wavelet coherency (PWC) has been proposed to detect scale-specific and localized bivariate relationships by excluding the effects of other variables but is limited to one excluding variable and provides no phase information. We aim to develop a new PWC method that can deal with multiple excluding variables and provide phase information. Both stationary and non-stationary artificial datasets with the response variable being the sum of five cosine waves at 256 locations are used to test the method. The new method was also applied to a free water evaporation dataset. Our results verified the advantages of the new method in capturing phase information and dealing with multiple excluding variables. Where there is one excluding variable, the new PWC implementation produces higher and more accurate PWC values than the previously published PWC implementation that mistakenly considered bivariate real coherence rather than bivariate complex coherence. We suggest the PWC method is used to untangle scale-specific and localized bivariate relationships after removing the effects of other variables in geosciences. The PWC implementations were coded with Matlab and are freely accessible (https://figshare.com/s/bc97956f43fe5734c784, last access: 14 January 2021).