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Öğe Assessing the impact of resource efficiency, renewable energy R&D spending, and green technologies on environmental sustainability in Germany: Evidence from a Wavelet Quantile-on-Quantile Regression(Elsevier, 2024) Özkan, Oktay; Eweade, Babatunde Sunday; Usman, OjonugwaOne important challenge in the world today is how to reverse the growth of carbon dioxide emissions to save the planet from environmental degradation without putting economic growth at risk. Several measures and initiatives such as resource efficiency, green energy transition, energy technologies, emission control, etc. Have been adopted by several countries worldwide in order to mitigate CO2 emissions. This study investigates the impact of resource efficiency, renewable energy Research and Development (R&D) expenditures, and green technologies towards fostering environmental sustainability in Germany. Using quarterly data spanning from 1974 to 2019, the study applies the Wavelet Quantile-on-Quantile Regression (WQQR) approach. This method embeds a wavelet kernel into quantile-on-quantile regression to capture the time-varying coefficients. The empirical findings reveal a negative impact of resource efficiency, renewable energy R&D expenditures, and green technologies on energy-based carbon intensity. The results further reveal that, with the exception of green technologies, the negative effects of resource efficiency and renewable energy R&D expenditures are stronger in the middle quantiles. The study demonstrates the robustness of these results through the wavelet quantile regression analysis. Finally, the study offers valuable policy implications that align with the United Nations’ Sustainable Development Goals (SDGs) 7 and 13, aiming to achieve a sustainable environment.Öğe A consideration of the environmental externality of Turkey's integration into global value chains: evidence from dynamic ARDL simulation model(SPRINGER HEIDELBERG, 2022) Olasehinde-Williams, Godwin; Özkan, OktayThe phenomenal growth experienced by Turkey at the turn of the millennium is attributed in part to increased participation in global value chains. While participation in global value chains has been benefcial to the Turkish economy, it also poses unique environmental challenges. Consequently, this study focuses on shedding some light on the environmental externality of Turkey’s participation in global value chains. This article examines the environmental efects of Turkey’s participation in global value chains for the period 1990–2018, using a dynamic ARDL analysis. The study further compares the environmental efects of Turkey’s backward and forward linkages into global value chains, so as to determine which contributes more to carbon emissions. The cointegration test results and dynamic ARDL simulations confrm the existence of a long-run relationship between the environment and global value chain participation. All measures of global value chain participation display a positive long-run impact on carbon emissions. The results also show that the polluting efect of backward and forward linkages into global value chains is not too diferent. The study fnding suggests that Turkey is being assigned segments of the value chain that require dirtier production processes through incentives from global trade integration, thus making Turkey a pollution haven. It is concluded that this is because other countries continually source for inputs requiring dirty production processes from Turkey, as Turkey also exports fnal goods that are produced using eco-unfriendly techniques. Policymakers in Turkey therefore need to follow more environmentalist policies in the process of global value chain participation.Öğe Dynamic connectedness of clean energy markets, green markets, and sustainable markets: The role of climate policy uncertainty(Elsevier, 2024) Özkan, Oktay; Sunday Adebayo, Tomiwa; Usman, OjonugwaThis study examines the dynamic connectedness of clean energy, green, and sustainable markets, and determine how climate policy uncertainty affects the level of connectedness in these markets. To this end, we use clean energy and clean technological innovation assets to represent clean energy markets; green bonds and clean cryptocurrency assets to represent green markets; and carbon and sustainability assets to represent sustainable markets. To analyze the connectedness, we apply a novel Quantile Connectedness measure to daily data ranging from January 9th, 2018 to September 11th, 2023. Results reveal a strong interconnectedness of clean energy markets, green markets, and sustainable markets. Results also show that clean energy markets are net transmitters of shocks, green markets are net receivers of shocks, and sustainable markets are both net transmitters and net receivers of shocks. Furthermore, to examine the role of climate policy uncertainty, we employ three different nonlinear methods, namely; the nonparametric causality-in-quantiles, quantile regression, and Kernel-based regularized least squares. Empirical results suggest that climate policy uncertainty has a causal and positive effect on the interconnectedness of clean energy, green, and sustainable markets. These findings are validated by various robustness analyses, and hence, provide vital insights into the risk diversification's goal of investors and portfolio managers.Öğe Dynamic risk connectedness of crude oil price and sustainable investment in the United States: evidence from DCC-GARCH(Springer, 2023) Olasehinde-Williams, Godwin; Özkan, Oktay; Akadiri, Seyi SaintSustainable investment is widely regarded as an important market-based approach to achieving inclusive green growth. To achieve the inclusive green growth objective, companies providing sustainable products must be proftable enough to attract private capital. Oil price changes can however afect the proftability of such companies. This study assesses volatility transmission between crude oil prices and sustainable investment in the USA. Using the dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) method, daily data from September 28, 2012, to October 19, 2022, is analyzed. There are several key fndings from this analysis. The risk connectedness of crude oil and sustainable investment is found to vary with time. Results further show that the risk connectedness increases in periods of important economic and geopolitical events. The greatest risk connectedness of crude oil and sustainable investment is observed during the outbreak of coronavirus disease (COVID-19). Moreover, the result shows that crude oil is the main risk transmitter, whereas, both the energy efciency and pollution mitigation indices (i.e., sustainable investment) are risk receivers, and crude oil is constantly dominating sustainable investment. The study fndings provide valuable insights for investors and policymakers alike.Öğe Effects of climate policy uncertainty on sustainable investment: A dynamic analysis for the U.S(Springer Science and Business Media Deutschland GmbH, 2023) Olasehinde-Williams, Godwin; Özkan, Oktay; Akadiri, Seyi SaintUncertainties surrounding climate change policies of the United States introduce some degree of risk into sustainable investment decisions in the country. This study is an attempt to provide a new perspective on the nature of this problem. Both the traditional and time-varying nonparametric quantile causality techniques are employed in investigating the effects of climate policy uncertainty on sustainable investment in the United States. Weekly time-series data from October 17, 2010, to August 28, 2022, is used for empirical analysis. Results from the traditional nonparametric quantile causality analysis reveal that climate policy uncertainty has a significant causal effect on both sustainable investment returns and volatility. The results also show that the impact on sustainable investment volatility is greater than the impact on sustainable investment returns. The time-varying nonparametric quantile causality analysis confirms that climate policy uncertainty in the United States affects both the returns and volatility of sustainable investment and that the impact is greater for volatility. It is recommended that governments and policymakers ensure that climate policy objectives are properly defined and adhered to, such that regulatory uncertainty would be limited and private sector participation in sustainable investment would be encouraged. İn addition, policies clearly designed to incentivize sustainable investment by integrating risk premiums into expected profits could be employed.Öğe Energy security-related risks and the quest to attain USA's net-zero emissions targets by 2050: a dynamic ARDL simulations modeling approach(Springer, 2024) Usman, Ojonugwa; Özkan, Oktay; Alola, Andrew Adewale; Ghardallou, WafaThe Russia-Ukraine war and other similar conflicts across the globe have heightened risks to the United States of America's (USA's) energy security. However, little is known about the severity of the effect of energy security risks on the USA's quest to attain net-zero emissions targets by 2050. To this end, we examine the effect of energy security risks on the load capacity factor (LCF) in the USA. Employing a time series dataset spinning from 1970 to 2018, the results of the Dynamic Autoregressive Distributed Lag (ARDL) simulations model suggest that energy security-related risk hampers the long-term net-zero emissions targets with its effect decreasing over time until it varnishes in about 5 years time. The results also show that foreign direct investment (FDI) inflows, renewable energy consumption, and green technology have long- and short-run positive effects on the LCF. Conversely, economic expansion and urbanization impede environmental quality by lowering the LCF both in the long run and short run. These findings are upheld by the outcomes of the multivariate quantile-on-quantile regression. Therefore, the study advocates for the consumption of renewable energy, investment in green technologies, and FDI inflows to mitigate energy security-related risks and attain the net-zero emissions targets by 2050 in the USA.Öğe Energy-related uncertainty shocks and inflation dynamics in the U.S: A multivariate quantile-on-quantile regression approach(Elsevier, 2024) Usman, Ojonugwa; Özkan, Oktay; Koy, Ayben; Adebayo, Tomiwa SundayExisting literature suggests that uncertainty shocks can propagate like aggregate demand shocks or aggregate supply shocks. By way of extension, this study investigates the effect of energy-related uncertainty shocks on U.S. inflation while incorporating the effect of industrial production and interest rate uncertainty shocks. Using a multivariate quantile-on-quantile regression for the period 2000:M6 to 2019:M7, the findings reveal that energyrelated uncertainty shocks amplify inflation by manifesting as cost-push shocks with a stronger connection emerging in quantiles slightly above the median quantile distribution of energy-related uncertainty. Although industrial production positively drives inflation, its effect is observed less around median quantiles of inflation than in the lower and upper quantiles. Furthermore, the effect of interest rate uncertainty is negative and stronger in quantiles around the median of inflation, suggesting that interest rate uncertainty behaves like aggregate demand shocks. Based on these findings, policy implications are offered.Öğe Examining crude oil price outlook amidst substitute energy price and household energy expenditure in the USA: A novel nonparametric multivariate QQR approach(Elsevier B.V., 2023) Alola, Andrew Adewale; Özkan, Oktay; Usman, OjonugwaThe outlook of crude oil prices has sparsely been empirically examined especially from the critical perspectives of energy expenditure per household, retail electricity prices, and environmental indicators. Given the enormous macroeconomic and socioeconomic effects of crude oil price amidst the fundamentals, this study examines the dynamics of the oil price outlook amidst energy demand (measured by energy expenditure per household), retail electricity price i.e., substitute price, and carbon dioxide (CO2) emissions in the United States of America (USA) over the period 1970 to 2040. This study offers two main innovations: first, it extends the bivariate nonparametric Quantile-on-Quantile Regression (QQR) to the multivariate case. Second, the analysis incorporates projected data series, which provides useful policy insights. The empirical results show evidence of time-varying effects of energy expenditure per household, retail electricity price, and CO2 emissions across the quantiles of crude energy prices. The results further show that the effect of energy demand through household energy expenditures is positive and stronger at the lower quantiles of crude oil price, which corresponds to periods of low crude oil prices. Furthermore, the effects of retail electricity price and CO2 emissions are negative and stronger in the mid-quantiles of crude oil price. This suggests that retail electricity prices and environmental indicator dampen crude oil prices during periods of low crude oil prices. These findings are robust to multivariate Quantile regression and Kernel-based Regularized Least Squares (KRLS) estimates. Therefore, our study suggests time-varying policies to dampen the effects of energy demand, retail electricity price, and environmental indicator on crude oil prices in the USA.Öğe Geopolitical oil price uncertainty transmission into core inflation: Evidence from two of the biggest global players(Elsevier, 2023) Lee, Chien-Chiang; Olasehinde-Williams, Godwin; Özkan, OktayThis research argues that inflation indirectly correlates with geopolitics through the oil markets. The argument is that uncertainties generated by geopolitics are often transmitted into core inflation through oil prices, and we provide empirical evidence to support this by establishing an uncertainty-oil-macroeconomy nexus for the biggest oil importing countries, the U.S. and China. The study specifically examines whether there are indirect contributions of geopolitical oil price uncertainty to inflation that appear in core inflation, excluding the more volatile food and energy prices. It employs a non-parametric quantile causality technique for analyzing how geopolitical oil price uncertainty and core inflation interact in both countries and conducts rolling windows based non-parametric quantile causality analysis for robustness. The full-time non-parametric quantile causal ity results show that geopolitical oil price risk strongly affects core inflation both in mean and variance, espe cially in the mid-quantiles, and that its effect is greater in variance relative to mean for both countries. The rolling windows-based outcomes indicate that geopolitical oil price risk exerts an increasing influence on core inflation during important geopolitical events such as the Euro crisis, Brexit, presidential elections, trade wars, and COVID-19, and these impacts differ not only between countries, but also according to whether causality is mean or variance. Finally, the significance of the findings is discussed.Öğe Geopolitical oil price uncertainty transmission into core inflation: Evidence from two of the biggest global players (vol 126, 106983, 2023)(Elsevier, 2024) Lee, Chien-Chiang; Olasehinde-Williams, Godwin; Özkan, Oktay[Abstract Not Available]Öğe Global evidence of multi-dimensional asymmetric effect of energy storage innovations on environmental quality: Delineating the role of natural resources, nuclear energy and oil consumption(Elsevier, 2024) Usman, Ojonugwa; Özkan, Oktay; Ike, George N.The strive to lower reliance on fossil fuels and transition to clean energy sources necessitates innovations in energy storage. This study empirically investigates the effectiveness of energy storage innovations towards a greener environment on a global scale. To this end, we use quarterly frequency time series data on a global scale over the period 2000 to 2020 and the estimation techniques based on the nonparametric multivariate quantile on quantile regression (MQQR), multivariate quantile regression (MQR), and Kernel-based regularized least squares (KRLS). The empirical results reveal that energy storage innovation and nuclear energy are strongly related to a decline in carbon dioxide emissions (CO2e) but natural resources and oil consumption intensify the level of CO2e across quantiles. However, the effects of energy storage innovation, nuclear energy, natural resources, and oil consumption are heterogeneous leading to an asymmetric pattern across quantile distribution. The policy implication of this study is that, on a global scale, energy storage innovation and nuclear energy provide opportunities for attaining a greener environment and more environmentally sustainable future but natural resources and oil consumption impede policies toward a sustainable environment. Therefore, our study recommends, among other things, an aggressive boost of energy storage system and nuclear energy while decreasing reliance on fossil fuels.Öğe Global evidence on the energy-environment dilemma: The role of energy-related uncertainty across diverse environmental indicators(Taylor & Francis Inc, 2024) Özkan, Oktay; Usman, Ojonugwa; Eweade, Babatunde SundaySeveral existing studies show that macroeconomic uncertainties intensify global environmental and climate challenges, putting the globe at risk of not being able to achieve the United Nations' Sustainable Development Goals by 2030. In this study, we provide global evidence on the role of energy-related uncertainty in the energy - environment dilemma between 1996 and 2021. We employ three distinct environmental indicators - load capacity factor (LCF), carbon dioxide emissions (CO2), and ecological footprint (EFP) - alongside a comprehensive global energy-related uncertainty index and time-frequency-quantile methods based on the Wavelet Quantile Correlation, Cross-Quantilogram, and Wavelet Local Multiple Correlation with Dominance. The empirical results suggest negative and strong nonlinear dependencies between energy-related uncertainty and the LCF across periods and quantiles. The results further suggest that the energy-related uncertainty has positive and strong nonlinear dependences not only with CO2 emissions but also EFP across various periods and quantiles. The results further suggest that the dependences between energy-related uncertainty and environmental indicators vary across periods and quantiles, with evidence of stronger dependency structures in the long run. These findings underscore the substantial influence of energy-related uncertainties on contemporary environmental challenges. We suggest that governments and policymakers need to reshape policy directives toward mitigating the environmental effects of energy-related uncertainties.Öğe Investigating the nexus between economic complexity and energy-related environmental risks in the USA: Empirical evidence from a novel multivariate quantile-on-quantile regression(Elsevier B.V., 2023) Özkan, Oktay; Haruna, Roselyn Afor; ALOLA, Andrew Adewale; Ghardallou, Wafa; Usman, OjonugwaThis study uses the economic complexity index to examine how knowledge accumulation and its uses affect energy-related environmental risks in the USA over the period 1995:Q1–2020:Q4. To this end, we extend the traditional bivariate Quantile-on-Quantile Regression to the multivariate case. The empirical results provide time-varying effects of economic complexity, economic growth, FDI, trade openness, and urbanization on energy-related environmental risks. Particularly, the effect of economic complexity is negative and weak in the extremely lower quantiles of energy-related environmental risks, while it is positive and stronger in the middle and higher quantiles. The implication of these results is that economic complexity only condenses energy-related environmental risks when such environmental risks caused by energy-related factors are extremely low. Furthermore, economic growth and tradeopenness stimulate energy-related environmental risks but the effects of FDI and urbanization reduce energy-related environmental risks. Therefore, these findings provide insights into achieving environmental sustainability targets in the USA.Öğe Is geopolitical oil price uncertainty forcing the world to use energy more efficiently? Evidence from advanced statistical methods(Elsevier B.V., 2024) Lee, Chien-Chiang; Olasehinde-Williams, Godwin; Özkan, OktayThis paper argues that energy efficiency is a potent shield against oil price uncertainty in an increasingly interconnected world fraught with geopolitical tensions. By reducing dependence on oil, enhancing economic resilience, and improving energy security, energy efficiency measures offer multifaceted benefits for both national economies and global stability. Specifically, a wavelet coherence analysis is conducted to study the response of global energy efficiency to geopolitical oil price uncertainty. Quantile-on-quantile and quantile regressions are additionally employed to separate the impacts of various geopolitical oil price risk quantiles on the quantiles of energy efficiency. These methods are utilized in the examination of global time-series data covering the timeframe 2004:Q1–2020:Q4. The wavelet coherence outcomes indicate a positive correlation between geopolitical oil price uncertainty and energy efficiency, particularly during the energy crisis of the 2000s and the COVID-19 pandemic. The results also reveal that geopolitical oil price uncertainty leads to energy efficiency, indicating that an upsurge in geopolitical oil price uncertainty causes energy efficiency to increase. Moreover, the results from quantile-on-quantile and quantile regressions affirm the predominantly positive effects of geopolitical oil price uncertainty on global energy efficiency. Our conclusion therefore is that through strategic investments, innovative policies, and international collaborations relating to energy efficiency, nations can fortify themselves against the destabilizing effects of geopolitical conflicts on energy markets. This would ensure a more sustainable and secure energy future for all.Öğe Is interest rate uncertainty a predictor of investment volatility? evidence from the wild bootstrap likelihood ratio approach(Springer, 2022) Olasehinde-Williams, Godwin; Özkan, OktayThis paper investigates the ability of interest rate uncertainty to predict investment volatility in nine selected countries. Employing a novel wild bootstrap likelihood ratio approach, the study shows interest rate uncertainty to be a signifcant predictor of investment volatility in all but one of the sampled countries. Overall, we fnd that interest rate uncertainty aggravates investment volatility in the United States, Germany, France, Italy, Spain, United Kingdom, Japan and Sweden. The results remain the same irrespective of whether a 3-month forecast horizon or a 12-month forecast horizon is used. The implication of the study fnding is that the current value of investments more often than not fuctuates in response to uncertain interest rate changes. This suggests that the investment rate is not only dependent on the interest rate level, but on the degree of uncertainty in interest rate movements as well. Interest rate uncertainty is thus an important factor to be considered in investment analysis. This study thus encourages central banks to pay signifcant attention to interest rate stability due to its ability to minimize the distortions in the market mechanism for raising long-term capital.Öğe Predicting stock returns and volatility in brıcs countries during a pandemic: evidence from the novel wild bootstrap likelihood ratio approach(Faculty of Social Sciences, 2022) Özkan, Oktay; Olasehinde-Williams, Godwin; Olanipekun, IfedolaIn this study, we examine how attention to different pandemics leads returns and volatility of BRICS stock markets, while controlling for economic policy uncertainty. The attention is measured via the newly developed daily infectious disease equity market volatility tracker (EMV-ID). To achieve the study objective, the wild bootstrap likelihood ratio test is employed in analysing time-series data covering the period November 1997 – May 2021. The estimations confirm a time-varying predictive performance of the EMV-ID on both stock returns and volatility series of BRICS, which increases significantly during the months marked by pandemics. The predictive power of the EMV-ID on stock market volatility is however relatively stronger than its predictive power on stock market returns. Our results are robust to alternative specification of volatility based on a Generalized Autoregressive Conditional Heteroskedasticity model.Öğe Role of household energy efficiency in shaping policy directives toward clean electricity transition in the United States: A nonparametric multivariate QQR approach(Elsevier, 2024) Usman, Ojonugwa; Nwani, Chinazaekpere; Özkan, OktayDespite the growing interest in energy transition policies, electricity generation in the U.S. remains heavily dependent on natural gas and coal. In this paper, we investigate the role of household energy efficiency in shaping policy directives toward a clean electricity transition in the U.S. through the use of a novel nonparametric multivariate quantile on quantile regression (M?QQR) over the period 1970 to 2040. The empirical results reveal that household energy-related efficiency has a positive effect on clean electricity transition across quantiles. This implies that household energy efficiency promotes clean electricity transition in the U.S. The results also show that energy expenditure and intensity of energy-related CO2 emissions are negatively related to clean electricity transition. However, the role of energy expenditures in the higher quantiles is positive. These findings align with the sensitivity and robustness analyses. This study offers significant contributions: First, the recently extended bivariate quantile-on-quantile regression to a multivariate case is applied. Second, we use historical and forecast datasets that span over seven decades. Overall, this study suggests the need for policymakers to utilize energy efficiency measures to stimulate transition towards clean electricity.Öğe Role of non-renewable energy efficiency and renewable energy in driving environmental sustainability in India: Evidence from the load capacity factor hypothesis(MDPI, 2023) Alola, Andrew Adewale; Özkan, Oktay; Usman, OjonugwaPolicymakers and environmental scientists have proposed numerous measures toward achieving a sustainable environment. Some of these measures include the efficient use of energy and a clean energy transition. This study empirically investigates the role of non-renewable energy efficiency and renewable energy utilization in driving environmental sustainability in India over the period from 1965 to 2018. Using the approach of the Dynamic Autoregressive Distributed Lag (DyARDL) simulations, the empirical evidence shows that non-renewable energy efficiency and renewable energy utilization promote environmental sustainability through an increase in the load capacity factor. The effects of financial development and trade impede environmental sustainability through a decrease in the load capacity factor. The results further show that the relationship between income and load capacity factor is characterized by an inverted U-shape. This suggests that the load capability curve (LCC) hypothesis is not valid for India. Given the overall findings of this study, it is suggested that policymakers should promote energy efficiency and renewable energy technologies as the ultimate policy measure to mitigate the accumulation of CO2 emissions and other significant climatic changes in India.Öğe Stock Market Response to Quantitative Easing: Evidence from the Novel Rolling Windows Nonparametric Causality-in-Quantiles Approach(Springer, 2023) Olasehinde-Williams, Godwin; Olanipekun, Ifedola; Özkan, OktayThe US Federal Reserve has been using quantitative easing as an unconventional monetary policy tool for providing liquidity and credit-market facilities to banks, and undertaking large-scale asset purchases in periods of crisis. This study carefully examines whether the US stock market has been responsive to the use of quantitative easing over time. A major contribution of this study to the extant literature is the introduction of the novel rolling windows nonparametric causality-in-quantiles approach to studying the reaction of the stock market to quantitative easing. This approach provides a means of investigating the time-varying causality between the variables across quantiles. The standard nonparametric causality-in-quantiles test results show that stock market performance is signifcantly predicted by quantita tive easing, except at very low and very high levels of stock returns (volatility). The rolling windows nonparametric causality-in-quantiles test results indicate that the causal efect of quantitative easing on stock market volatility and returns becomes pronounced during periods of crisis. The reactions are most signifcant in periods corresponding to the Asian fnancial crisis, the global fnancial crisis and the COVID-19 pandemic outbreak. Overall, the causal efect of quantitative easing on both stock market returns and volatility changes through time; the efect on stock market returns is also greater than on stock market volatility.Öğe The time-frequency-quantile causal impact of Cable News-based Economic Policy Uncertainty on major assets returns(Investment Analysts Society of South Africa, 2024) Adebayo, Tomiwa Sunday; Özkan, Oktay; Sofuoğlu, Emrah; Usman, OjonugwaAfter the collapse of the equity market in the early 2000s, the question of the drivers of financial assets returns preoccupied the interest of investors and policymakers in financial markets. Thus, this study explores how newly developed Cable News-based Economic Policy Uncertainty (TVEPU) predicts major assets returns using daily data from 1 January 2014 to 30 September 2023. To achieve this objective, we introduced the Rolling Windows Wavelet Quantile Granger Causality (RWWQGC) test. The empirical results show that TVEPU tends to have predictive power for SP500 across time, frequency, and quantile. The results also show that TVEPU has a strong causal impact on major financial assets returns across time, frequency, and quantile. However, the predictive power of TVEPU for the US 10-year bond, US dollar index, and Bitcoin is weak across time, frequency, and quantile. Based on these results, policy recommendations are offered.
