High Quantiles Research Articles

Moving toward sustainable goals 7 and 13: An inclusive mechanism to achieve environmental sustainability through digitalization and energy transition in OECD countries

In line with Sustainable Development Goals (SDGs) 7 and 13, this study proposes a policy framework while considering digitalization as a critical tool in shaping the energy transition process to attain environmental suitability in OECD countries. The study employed multifaceted empirical techniques, including Method of Moment Quantile Regression (MMQR), Fully Modified Ordinary Least Squares (FMOLS), and Dumitrescu and Hurlin (D-H), augmented with robustness tests over the period 2000 to 2021. The results indicate that digitalization augments energy transition and green finance to attain environmental sustainability. However, moving toward higher quantiles (4th, 6th, and 8th), the total impact of energy transition and digitalization is diminished. Besides that, a bidirectional causal relationship was reported running from green finance and digitalization to greenhouse gas (GHG) emissions. This study offers a detailed policy framework while considering SDGs 7 and 13.

Hedge and safe haven functions of gold and Bitcoin around COVID-19: evidence from U.S. financial assets

ABSTRACT The COVID-19 outbreak triggered an unparalleled health crisis and financial shock. We re-examine whether gold and Bitcoin can function as hedges and safe havens for U.S. financial assets before and after the COVID-19 outbreak. This study employs the quantile-on-quantile and causality-in-quantiles methods to detect the nonlinear and asymmetric relationship of gold and Bitcoin with U.S. financial assets. The results reveal negative dependence of the U.S. dollar, real estate, crude oil, and natural gas on gold and Bitcoin in some quantiles during both periods, indicating that gold and Bitcoin are hedges for these four assets. Following the COVID-19 outbreak, the negative correlations that exist for stock and clean energy with Bitcoin almost all turn positive, gold and Bitcoin lose their ability to hedge stocks and clean energy. Bitcoin can still hedge bonds in middle and high quantiles, whereas gold does not possess this capability against the bond. Additionally, there is an asymmetric causality in the mean and variance from U.S. financial assets to gold and Bitcoin, which generally exists in the middle quantiles but not in the extreme (high and low) quantiles. Our findings provide clear guidelines to market participants on risk management and policy decisions according to market conditions.

Coupling higher-order probability weighted moments with norming constants method for non-stationary annual maximum flood frequency analysis

Non-stationary flood frequency analysis (NFFA) is essential for reducing the risk of hydrologic engineering design and operation, especially when done under the severe influences of climate change and strong human activities. Although different types of methods have been proposed, NFFA is still a challenging task due to the complex characteristics of hydroclimatic data and weaknesses of diverse methods. In this article, a new method, called HPWM-NCM, is proposed for NFFA. The method uses the higher-order probability weighted moments (HPWM) method to accurately estimate parameters of flood data, and then applies the norming constants method (NCM) to precisely calculate the norming constants and further do NFFA. Results of both Monte-Carlo experiments and observed hydrologic data at 62 stations in the Rhine River basin illustrated that HPWM-NCM significantly improved the NFFA results compared to conventional NCM and the probability weighted moments method, as evaluated by overall fitting bias, residuals’ correlation, and residuals’ maximum error. Results highlighted the importance of using the information in high quantiles (rather than low quantiles) of flood data for NFFA, which is the key of the proposed method and ensures its superiority. Overall, the advantages of HPWM-NCM for non-stationary flood frequency analysis were confirmed, and the method has the potential for wide use in hydrologic and climate sciences.

The nexus between technological innovation, human capital and energy efficiency: Evidence from E7 countries

Enhancing energy efficiency is very effective in supporting environmental sustainability. Financial inclusion, human capital, and technological innovation are newly cited as dominant factors for environmental sustainability in emerging economies. Against this background, this study investigated how financial inclusion and technological innovation affected energy efficiency in E7 countries from 2004 to 2021. The study identifies the diverse effect of variables at different quantiles through a novel Moments Quantile Regression (MM-QR) method to explore the heterogeneous interconnection among the variables. The MMQR outcomes suggest financial inclusion positively affects energy efficiency across all quantiles. Besides, the findings show that human capital positively affects energy efficiency in E7 countries at lower, medium, and higher quantiles. At the same time, estimated outcomes suggest that technological innovation promotes energy efficiency only at lower quantiles. This study recommends appropriate policies for decision-makers in E7 economies based on the study outcomes regarding adequately channelling human capital, financial resources, and technological innovation to promote energy efficiency and achieve sustainable development goals.

Does an environmental stringent policy really matter to achieve environmental sustainability in BRICS-T region? Evidence from novel method of moments quantile regression approach

Amidst pressing global environmental challenges, exacerbated by climate change and the imminent threat of global warming, there is a critical need to assess the efficacy of environmental policies. This study centers its attention on the pivotal role of these policies in addressing environmental concerns. Specifically, our research aims to scrutinize the impact of stringent environmental policies on environmental quality under the theoretical underpinnings of environmental Kuznets curve. To achieve this objective, the study collected data from BRICS-T economies over the period of 1990–2020. This study employed the method of moments quantile regression technique for empirical analysis. Our study validates the presence of the Environmental Kuznets curve (EKC hypothesis). Empirical findings reveal the sustained significance of environmental stringency across all quantiles, demonstrating a positive correlation in lower quantiles and a negative correlation in higher quantiles. At lower quantiles, the impact is insignificant initially, but pronounced due to efficiency improvements induced by stringent policies. The effects became negative at middle quantiles, indicating stringent policies might encounter diminishing returns where policy measures start stabilizing ecological impacts. At higher quantiles, the influence of ESI remains significant, reflecting ongoing adaptations in larger economies with higher ecological footprints. This suggests the potential effectiveness of stringent regulatory measures in mitigating environmental impacts and reducing ecological footprints. The identified inverted U-shaped curve signifies that while stringent policies may not inherently enhance environmental health, beyond a certain threshold, they can indeed contribute to its improvement. Our policy recommendation advocates for the widespread adoption and promotion of such stringent measures to safeguard environmental health.

Nonlinear effect of urban noise pollution on depression of the elderly in China based on the Bayesian machine learning method

With the intensification of urbanisation and population ageing globally, it is crucial to understand the effect of urban noise pollution on the mental health of the ageing population. This research employed the Bayesian machine learning method to investigate the nonlinear effect of urban noise pollution on depression scores in the elderly (DSE) population in China. Based on individual survey data, the China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2020, and corresponding temporally monitored annual urban equivalent sound pressure level (ESPL) data in China, propensity score matching (PSM) was used to identify the causal effect of urban ESPL on DSE. The quantile regression model (QRM), the Bayesian additive regression tree model (BARTM), and the Bayesian causal forest method (BCFM) were used to detect the nonlinear influencing mechanism of urban ESPL on DSE. The PSM analysis which can balance confounding influence revealed a significant disparity in depression scores between ageing individuals exposed to higher ESPL and those residing in more tranquil settings, suggesting a definitive causal relationship. Furthermore, the QRM results highlight a significantly increasing effect of ESPL on DSE at higher quantiles (0.4528 and 0.5709 at the 75th and 85th percentiles, respectively). The BARTM identifies a ‘U-shaped’ dose–response curve between ESPL and DSE, with low ESPL as beneficial, but exposure above 53.67 dB(A) representing noise pollution, especially past 55.85 dB(A), significantly increasing DSE. Specifically, when ESPL ranges from 53.67 to 55.68 dB(A) with other variables held constant, DSE increases by an average of 0.28 for every 1 dB(A) rise. Beyond 55.68 dB(A), the average DSE increase is 0.85 for each additional 1 dB(A) in ESPL. A notable spatial heterogeneity was observed in the local effects of annual urban ESPL on DSE. The BCFM results indicate that socio-economic exposure level can modulate the effect of ESPL on DSE.

Impact of risk factors on the link between natural resources rents and carbon emissions: Evidence from economic, financial, and political risks

Effective management of natural resources is crucial for diminishing carbon emissions. This research explores how economic, financial, and political risks influence the relationship between natural resources rents and carbon emissions. Analyzing data from 66 countries, this study utilizes methods such as quantile regression and dynamic threshold regression to thoroughly assess the data. The findings reveal: (i) Natural resources rents tend to increase carbon emissions consistently across different quantiles (0.1 to 0.9). The fact is confirmed by robustness checks, illustrating that increased natural resources rents lead to higher emissions. (ii) Economic, financial, and political risks affect how natural resources rents impact carbon emissions. Notably, reduced economic and financial risks lessen the propensity of natural resources rents to boost emissions at higher quantiles, while a decline in political risk decreases the exacerbating effect of natural resources rents on emissions from the 0.1 to 0.9 quantiles. (iii) This analysis uncovers threshold effects where economic, financial, and political risks act as threshold factors. Specifically, when economic and political risks are low, a rise in natural resources rents actually leads to a decline in carbon emissions. The findings underscore the importance of considering these risks in the formulation of policies aimed at reducing carbon emissions from natural resource exploitation.

How does geopolitical risk affect clean energy generation? Daily evidence from five highly Russia-reliant European countries

Europe has recently experienced an energy crisis that began with the war between Russia and Ukraine and continued with reciprocal sanctions and power cuts, increasing geopolitical tensions and risks. In such an environment, European countries must decide how to replace energy imports from Russia. As the best option may be to rely on clean energy, this study explores how geopolitical risk (GPR) affects clean electricity generation (i.e., hydro, solar, wind, & nuclear) in five European countries (i.e., Bulgaria, Czechia, Germany, Romania, and Switzerland), which have a high dependency on Russia. The study therefore uses daily data between January 2, 2019 and January 29, 2024 and applies a set of nonlinear methods (i.e., wavelet coherence, quantile-on-quantile regression, Granger causality in quantiles, and quantile regression). The results show that (i) there is a strong dependence of GPR on renewable and nuclear electricity generation over different times, frequencies, and countries; (ii) GPR stimulates hydro electricity generation at higher quantiles across countries except the Czechia; (iii) GPR increases solar electricity generation at higher quantiles in all countries except the Czechia and Switzerland; (iv) GPR mainly stimulates wind electricity generation at higher quantiles, except for Bulgaria and Switzerland; (v) GPR is almost inefficient in nuclear electricity generation. Overall, the study clearly shows the supportive effect of the GPR in promoting renewables, while this is not the case for nuclear energy. Therefore, European countries can rely on renewables as the best alternative against energy blackouts in the short term, considering the varying effect based on time, frequency, quantile, country, and clean energy sources.

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

AbstractProjecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

Investigating the persistence degree of environmental patent: application of panel SPSM-quantile unit root test

ABSTRACT This article analyzes the degree of persistence of shocks to environmental patents in the OECD countries from 1980 to 2022 by applying the panel quantile unit root (PQUR) test and sequential panel selection (SPS) method. Our results indicate that most OECD countries reveal similar stochastic behaviours, where low and extreme (middle and moderate high) quantiles behave as unit root (stationary) processes. According to our findings, except for the US, the time series of environmental patents of other OECD countries behaved as a unit root process in 2022. Accordingly, the mandatory and incentive pro-green innovation policies have long-lasting effects on the development of the patents.

Impact of carbon market prices on oil market fear across quantiles

ABSTRACT This paper uses quantile regression to investigate how carbon market prices affect oil market fear. Our results show a significant and strong negative effect of changes in carbon market prices on changes in oil market fear at high quantiles. Notably, this negative effect is driven by the decrease rather than the increase in carbon market prices. We also observe that the COVID-19 pandemic enhances the negative impact of the decreased carbon market prices at high quantiles.

The relationship between the systemic immune inflammation index and the nonalcoholic fatty liver disease in American adolescents

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a growing health crisis in the general population of the United States (U.S.), but the relationship between systemic immune-inflammation (SII) index and NAFLD is not known.MethodsWe collected data from the National Health and Nutrition Examination Survey 2017–2018. Next, propensity score matching (PSM), collinearity analysis, restricted cubic spline (RCS) plot, logistic regression, quantile regression analysis, subgroup analysis, mediation analysis, and population attributable fraction were used to explore the association of the SII with risk of NAFLD.ResultsA total of 665 participants including the 532 Non-NAFLD and 133 NAFLD were enrolled for further analysis after PSM analysis. The RCS results indicated that there was a linear relationship between the SII and controlled attenuation parameter (p for nonlinear = 0.468), the relationship also existed after adjustment for covariates (p for nonlinear = 0.769). The logistic regression results indicated that a high SII level was an independent risk factor for NAFLD (OR = 3.505, 95% CI: 1.092–11.249, P < 0.05). The quantile regression indicated that at higher quantiles (0.90, and 0.95) the SII was significantly associated with NAFLD (p < 0.05). Mediation analysis indicated that alanine aminotransferase (ALT), triglycerides, and blood urea nitrogen (BUN) were partially contribute to the relationship between SII and NAFLD. The population attributable fractions indicated that 23.19% (95% CI: 8.22%, 38.17%) of NAFLD cases could be attributed to SII corresponding to 133 NAFLD cases.ConclusionThere was a positive linear relationship between the SII and the risk of NAFLD. The ALT, triglycerides, and BUN had a partial mediating effect on the relationship between the SII and NAFLD.

The Impact of Financial Efficiency and Renewable Energy Consumption on CO2 Emission Reduction in GCC Economies: A Panel Data Quantile Regression Approach

As prominent oil producers, Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world’s attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC’s economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. This study employs quantile regression, a robust statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several essential insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th, −83,537.3) and higher quantiles (90th, −549,002.3). The relationship between the GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth–environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a more substantial negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. This study’s outcomes hold crucial policy implications for GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions.

The role of renewable energy and carbon dioxide emissions on the ESG market in European Union

AbstractIn view of global climate problems, public interest in the environment has recently evolved over decision economics. Accordingly, this study assesses the impact of carbon emission allowances (CEA), information technology (IT), renewable energy generation (REG), and carbon dioxide (CO2) on the environmental, social, governance (ESG) in the European Union (EU) by applying quantile‐based models from January 2, 2019 to February 29, 2024. The outcomes demonstrate that CEA and IT have an increasing effect on the ESG with the moderating effect of economic policy uncertainty (EPU). REG has a declining effect on the ESG, while the EPU moderates the effect and makes the effect increasing across higher quantiles.

Exploring the nexus of geopolitical risk, green financing, and natural resource rents: A study of Russia's economic growth

Geopolitical risk encompasses various uncertainties arising from political, social, and economic factors, which can significantly influence a country's economic performance. Therefore in this study we analyzed the impact of geopolitical risk (GPR), green financing, and natural resource rents (NRR) on Russia's economic growth during 2000–2022 is examined in this study. The collected data is analyzed using simple quantile regression, the ADF unit root test, Johansen Co-integration, and other time series methods. A unit root test shows that level (1) data is stable and that variables are co-integrated. Quantile regression research shows that GPR positively impacts economic growth at the median and higher quantiles. In addition, green finance and commerce boost economic growth across all quantiles. The results support the resource curse hypothesis, which claims that NRR negatively affects economic growth. The Granger causality test shows that GDP and its explanatory components are causally related. These findings were compatible with quantile regression estimates after a parametric examination. The policy implications for researchers and politicians concerned with green finance, natural resource sustainability, and geopolitical dangers to economic growth are significant.

Directional connectedness between the electricity prices and natural gas prices: evidence from Alberta’s electricity market

Purpose This study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors. Design/methodology/approach The methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023. Findings The main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value. Originality/value The study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

Connectedness between Sustainable Investment Indexes: The QVAR Approach

We studied the relationship between sustainable investment indexes and examine whether this relationship varies in bullish, bearish, and stable financial markets. To understand this issue more deeply, we analyzed the connectedness between three indexes—the Sustainable Impact investments, Paris-aligned stocks, and green bonds indexes—using the daily closing prices from 1 June 2017 to 15 April 2024, encompassing 1793 observations. We used a quantile vector autoregressive (QVAR) model to understand the dynamic relationship among the considered indices. The findings indicate that sustainable investments are strongly interconnected in both high and low quantiles, but this connection weakens significantly during periods of market stability. The Sustainable Impact investments and Paris-aligned stocks indexes are net transmitters of impacts to other sustainable alternatives, while the green bonds index is a net receiver. We also observed an increase in interconnectedness across all quantiles during the pandemic, the Russia–Ukraine military conflict, and changes in the European Union and the United States’ monetary policies.

Quantile-based extended joint connectedness between trade policy uncertainty and Gulf Cooperation Council Islamic sectoral volatility

This study quantifies the shock transmission mechanism between the trade policy uncertainty (TPU) index and Sharia-compliant stock sectoral conditional volatility in the Gulf Cooperation Council (GCC) countries. We employ a comprehensive analysis that includes the time-domain extended joint and frequency-domain quantum vector autoregressive (QVAR) frameworks. The time-domain QVAR demonstrates that TPU causes the most substantial shocks to utility sector volatility. Investors in the GCC must strategically allocate their investment portfolios to the consumer services and energy industries, which are less vulnerable to the TPU shock. The results show that TPU shocks provide more error variances to the long-term and bullish conditional volatility (or higher quantiles) of the utility, real estate investment trust funds (REIT), healthcare, and industry sectors. Conversely, during bearish volatility conditions (lower quantiles), TPU shocks result in higher shocks in the conditional volatility of the utility, REIT, finance, and industrial sectors. Long-term investors should diversify their portfolios to mitigate risk in the utility, REIT, and industry sectors through strategic investments in consumer service sector. However, in the short term and during both bearish and bullish sectoral volatility, the consumer service and industry sectors are more vulnerable to TPU shocks. Regarding diversity, the REIT, energy, and financial sectors are the least affected by TPU shocks, offering a protective buffer in the short term and during periods of higher and lower GCC Islamic sectoral volatility.

Compounded wind gusts and maximum temperature via semiparametric copula in the risk assessments of power blackouts and air conditioning demands for major cities in Canada

A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation (GKDE) with parametric copulas. Their joint probability estimates allow for a better understanding of the risk of power blackouts and the demand for air conditioning in the community. The bivariate framework used two extreme sample groups to define extreme pairs at different time lags, i.e., 0 to ± 3 days, annual maximum WGS (AMWGS) and corresponding MT and annual highest MT (AHMT) and corresponding WGS. A thorough model performance comparison indicated that GKDE outperformed the parametric models in defining the marginal distribution of selected univariate series. Significant positive correlations were observed among extreme pairs, except for Calgary and Halifax stations, with inconsistent correlation variations based on selected cities and lag time. Various parametric 2-D copulas were selected to model the dependence structure of bivariate pairs at different time lags for selected stations. AMWGS or AHMT events, when considered independently, would be stressful for all stations due to high estimated quantiles with low univariate RPs. The bivariate events exhibited lower AND-joint RPs with moderate to high design quantiles, indicating a higher risk of power blackouts and heightened air-conditioning demands, which varied inconsistently with time lags across the station. The bivariate AMWGS and corresponding MT events would be stressful in Regina, Quebec City, Ottawa, and Edmonton, while AHMT and corresponding WGS events in Toronto, Regina, and Montreal. Conversely, Vancouver poses a lower risk of joint action of pairs AHMT and corresponding WGS events. These hazard statistics can help in better planning for community well-being during extreme weather.

Nghiên cứu tác động của chính sách an toàn vĩ mô tới khả năng sinh lời- Bằng chứng thực nghiệm tại các ngân hàng thương mại Việt Nam

This paper investigates the impact of macroprudential policies on the profitability of 27 Vietnamese commercial banks from 2014:Q3 to 2023:Q3. This paper uses panel data regression methods combined with tests to resolve the endogeneity issue, and robustness checks produce consistent research results. Accordingly, empirical results show that contractionary macroprudential policies have a positive impact on the profitability of Vietnamese commercial banks. The results from the quantile regression model indicate that contractionary macroprudential policies help commercial banks with low ROA quantiles improve profitability better than banks with high ROA quantiles. Based on that, this research forms several recommendations including: the banks should conduct appropriate strategies to align to macroprudential regulations; the State Bank of Vietnam is in charge of the banks in complying to these regulations.