A comprehensive analysis of key factors' impact on environmental performance: Evidence from Globe by novel super learner algorithm.

This study aims to analyze comprehensively the impact of different economic and demographic factors, which affect economic development, on environmental performance. In this context, the study considers the Environmental Performance Index as the response variable, uses GDP per capita, tariff rate, tax burden, government expenditure, inflation, unemployment, population, income tax rate, public debt, FDI inflow, and corporate tax rate as the explanatory variables, examines 181 countries, performs a novel Super Learner (SL) algorithm, which includes a total of six machine learning (ML) algorithms, and uses data for the years 2018, 2020, and 2022. The results demonstrate that (i) the SL algorithm has a superior capacity with regard to other ML algorithms; (ii) gross domestic product per capita is the most crucial factor in the environmental performance followed by tariff rates, tax burden, government expenditure, and inflation, in order; (iii) among all, the corporate tax rate has the lowest importance on the environmental performance followed by also foreign direct investment, public debt, income tax rate, population, and unemployment; (iv) there are some critical thresholds, which imply that the impact of the factors on the environmental performance change according to these barriers. Overall, the study reveals the nonlinear impact of the variables on environmental performance as well as their relative importance and critical threshold. Thus, the study provides policymakers valuable insights in re-formulating their environmental policies to increase environmental performance. Accordingly, various policy options are discussed.

Role of energy transition in easing energy security risk and decreasing CO2 emissions: Disaggregated level evidence from the USA by quantile-based models.

Consistent with the increasing environmental interest, the clean energy transition is highly critical to achieving decarbonization targets. Also, energy security has become an important topic under the shadow of the energy crisis,. Accordingly, countries have been trying to stimulate clean energy use to preserve the environment and ensure energy security. So, considering the leading role of economic size and volume of energy use, the study examines the USA to define whether energy transition helps decrease energy security risk (ESR) and curb CO2 emissions. So, the study applies a disaggregated level analysis by performing quantile-based models for the period from 2001/Q1 through 2022/Q4. The results demonstrate that (i) the energy transition index decreases environmental ESR at higher quantiles and reliability ESR at lower and middle quantiles, whereas it is not beneficial in declining economic and geopolitical ESR; (ii) energy transition curbs CO2 emissions in building and transport sectors at lower quantiles, whereas it does not help decrease CO2 emissions in industrial and power sectors; (iii) energy transition is mostly ineffective on ESR, whereas it is highly effective in curbing CO2 emissions in all sectors except for transport across various quantiles as time passes; (iv) the results differ according to the aggregated and disaggregated levels; (v) the results are consistent across main and alternative models. Hence, the study highlights the dominant effect of energy transition in curbing sectoral CO2 emissions rather than easing ESR. Accordingly, the study discusses various policy implications for the USA.

Natural gas supply cuts and searching alternatives in Germany: A disaggregated level energy consumption analysis for environmental quality by time series approaches.

By considering the search for alternatives against Russia's natural gas supply cuts, this study explores the impact and causality of disaggregated level energy consumption indicators on environmental quality. Hence, the study investigates Germany, which is the leading economy in Europe and highly dependent on Russia's natural gas supply, by using carbon dioxide (CO2) emissions as the environment indicator, including annual data from 1970 to 2021, and applying novel time series approaches. In the empirical examination, Granger causality-in-quantiles (GCiQ), quantile-on-quantile regression (QoQR), and multivariate adaptive regression splines (MARS) are applied as base models while quantile regression (QR) and dynamic ordinary least squares (DOLS) are used for robustness. The empirical findings show that (i) there are causal impacts of disaggregated level energy consumption indicators on CO2 emissions; (ii) renewable energy and hydroelectricity consumption have a decreasing impact, whereas natural gas, coal, and oil energy consumption have an increasing impact on CO2 emissions; (iii) although nuclear energy has been discussed as a potential alternative, nuclear energy does not have a significant impact in decreasing CO2 emissions; (iv) natural gas consumption has an interaction with renewable energy, hydroelectricity, and coal energy consumption; (v) the power of disaggregated level energy consumption indicators on CO2 emissions vary according to quantiles, thresholds, and interactions between energy consumption indicators; (iv) alternative models validate robustness of the results obtained. Thus, the results imply that the most appropriate alternative is coal energy consumption in the short-term and renewable energy consumption in the long-term to compensate for Russia's natural gas supply cuts, whereas nuclear energy consumption is not a real alternative for Germany.

Can environmentally friendly technology help China to achieve a carbon neutrality target by 2060? An asymmetrical based study in China.

Climate change-related environmental challenges are prompting an increasing number of countries to set carbon-neutral targets. Since 2007, China has pursued numerous initiatives to attain carbon neutrality by 2060, including increasing the percentage of non-fossil energy, developing zero-emission and low-emission technologies, and taking actions that reduce CO2 emissions or boost carbon sinks. As a result, utilizing quarterly data from 2008/Q1 to 2021/Q4, and applying the nonlinear autoregressive distributed lag (NARDL) approach, this study evaluates the effectiveness of the measures taken by China to improve the ecological situation. The results of the study show that the measures enacted to reduce CO2 emissions did not accomplish their ultimate purpose. Specifically: (i) high-speed railways and new energy vehicles do not improve the environment in the long run; (ii) investments and patents in the energy sector, as well as low-carbon sources, will degrade the environment; (iii) only investments in the treatment of environmental pollution will improve the ecological situation. Various policy implications are suggested based on the empirical results in order to attain environmental sustainability.

Dynamic link between central bank reserves, credit default swap spreads, and foreign exchange rates: Evidence from Turkey by time series econometrics.

In this study, dynamic links between central bank reserves (CBR), credit default swap (CDS) spreads, and foreign exchange (FX) rates are investigated. So, Turkey, which is a negative outlier country among other peer emerging countries, is examined by considering recent developments on these indicators. In doing so, the study covers relatively high frequency (i.e., weekly) data from January 2, 2004 to November 12, 2021, performs various econometric approaches as Wavelet Coherence (WC), Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ) as main models, and applies Toda-Yamamoto (TY) causality and Quantile Regression (QR) for the robustness. The results show that (i) there is a time-frequency dependency between the CBR, CDS spreads, and FX rates; (ii) a bidirectional link exists between the CBR and FX rates; between the FX rates and CDS spreads; and between the CDS spreads and CBR; (iii) the link exists in most quantiles except for some lower and middle quantiles for some indicators; (iv) explanatory effect of the indicators on each other varies based on quantiles; (v) the robustness of the results are validated by the TY causality test for the WC model and by the QR approach for the QQR model. The results suggest the significance of the CBR for the FX rates, the FX rates for the CDS spreads, and the CDS spreads for the CBR.

Estimation of Impacts of Global Factors on World Food Prices: A Comparison of Machine Learning Algorithms and Time Series Econometric Models.

It is a well-felt recent phenomenal fact that global food prices have dramatically increased and attracted attention from practitioners and researchers. In line with this attraction, this study uncovers the impact of global factors on predicting food prices in an empirical comparison by using machine learning algorithms and time series econometric models. Covering eight global explanatory variables and monthly data from January 1991 to May 2021, the results show that machine learning algorithms reveal a better performance than time series econometric models while Multi-layer Perceptron is defined as the best machine learning algorithm among alternatives. Furthermore, the one-month lagged global food prices are found to be the most significant factor on the global food prices followed by raw material prices, fertilizer prices, and oil prices, respectively. Thus, the results highlight the effects of fluctuations in the global variables on global food prices. Additionally, policy implications are discussed.

Asymmetric effect of economic policy uncertainty, political stability, energy consumption, and economic growth on CO2 emissions: evidence from G-7 countries.

This study deals with the asymmetric effect of economic policy uncertainty and political stability on carbon dioxide (CO2) emissions considering also energy consumption and economic growth. In this context, the study investigates G-7 countries, which make up an important part of the world economy. Also, the study uses yearly data between 1997 and 2021 as the most available intersection data for all countries included. Besides, this study applies a novel nonlinear approach as quantile-on-quantile regression (QQR) as the base model, and quantile regression (QR) is used for robustness. The empirical results present that (i) economic policy uncertainty has a decreasing effect on CO2 emissions in Italy, Japan, and the United States of America (USA), whereas it has a mixed effect in Canada, France, Germany, and the United Kingdom (UK); (ii) political stability also has a mixed effect on CO2 emissions; (iii) energy consumption has an accelerating effect on CO2 emissions while the power of effect changes at quantiles; (iv) economic growth has generally an increasing effect on CO2 emissions, whereas it has a decreasing effect at lower quantiles in Japan, at middle quantiles in France and Germany, and at higher quantiles in Italy; and (v) the QR results support the robustness of QQR findings. Thus, the empirical results highlight that G-7 countries should consider the asymmetric and quantile-based varying effects of the economic policy uncertainty, political stability, and economic growth to reach their carbon neutrality targets.

Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA.

By considering the existence of two separate analysis families and the usage of different data frequencies, this study aims to examine the effect of method choice, data frequency, and sector-based energy consumption on carbon dioxide (CO2) emissions by performing machine learning (ML) algorithms and time series econometric (TS) models simultaneously. In this situation, the study examines the United States (USA), considers sector-based energy consumption indicators as explanatory variables, uses monthly and yearly data between January 1973 and December 2021, estimates CO2 emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R2 and goodness of fit criteria; (ii) the estimation performance of the models increases with the high-frequency (i.e., monthly) data; (iii) the ML algorithms perform much better in case of high-frequency usage; (iv) some thresholds identify the effects of the sector-based energy consumption indicators on the CO2 emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO2 emissions for monthly and yearly data, respectively. Hence, the study provides to help the understanding role of method choice, data frequency, and sector-based energy consumption for the estimation of CO2 emissions. Based on the results, this study proposes that US policymakers should consider the ML algorithms, use higher-frequency data, and include sector-based energy consumption indicators to have a better estimation of CO2 emissions.

Asymmetric effect of political stability on production-based CO2 emissions in the UK: long-run evidence from nonlinear ARDL and frequency domain causality.

The study deals with the effect of political stability on environmental degradation in the long run for the United Kingdom (UK). For this aim, the political stability effect on production-based carbon dioxide (CO2) emissions is examined by considering trade openness, renewable energy, and economic growth and using quarterly data between 1995/Q1 and 2018/Q4. Nonlinear autoregressive distributed lag (NARDL), which allows the researcher to measure the asymmetric impact of explanatory indicators positively or negatively, is performed as the empirical approach. Also, Breitung & Candelon (BC) frequency domain causality test is applied to measure the causality effect of explanatory variables on CO2 emissions. The results reveal that (i) political stability has a statistically significant effect on production-based CO2 emissions and positive shocks have a higher power than negative shocks; (ii) economic growth has an increasing effect, whereas renewable energy has a decreasing effect on production-based CO2 emissions; and (iii) there is frequency domain causality from political risk, economic growth, renewable energy consumption, and trade openness to production-based CO2 emissions. Hence, empirical results highlight the asymmetric effect of political stability on environmental degradation in the long run for UK. Thus, UK policymakers should consider political stability in policy development and implementation process for limiting CO2 emissions in the long run.

Heterogeneous impact of environmental taxes on environmental quality: Tax domain based evidence from the nordic countries by nonparametric quantile approaches.

This study aims to examine the heterogeneous causality and impact of environmental taxes at both aggregated and disaggregated levels on environmental quality. In this context, the study focuses on Nordic countries as green economies; handles carbon dioxide (CO2) emissions as an environmental quality indicator; includes aggregated and disaggregated levels of environmental taxes as explanatory variables; uses quarterly data for the period 1994/Q1-2020/Q4 as the most recent available data; applies novel nonparametric Granger causality-in-quantiles (GCQ) and quantile-on-quantile regression (QQR) approaches as the main models while using quantile regression (QR) for robustness check. The results present that (i) causal impacts of environmental taxes on CO2 emissions exist in most quantiles at disaggregated levels excluding some lower, middle, and higher quantiles, whereas indicator-, country-, and quantile-based results vary; (ii) environmental tax on energy (ETE) has a mainly decreasing impact in Iceland, a mixed impact in Denmark, Finland, Norway, and Sweden based on quantiles; (iii) environmental tax on pollution (ETP) has the highest decreasing impact in most quantiles in Denmark, Iceland, and Norway; (iv) environmental tax in transport (ETT) has a decreasing impact in Norway and Sweden, whereas it has a reverse impact in Denmark, Finland, and Iceland; (v) impact of total environmental tax (TET) has a decreasing impact in Denmark and Norway at some quantiles, whereas an increasing impact in Finland, Iceland, and Sweden; (vi) the robustness of the QQR results are confirmed by the QR approach. Hence, the results underline the importance of country and quantile-based disaggregated analyses and Nordic countries should re-adjust environmental taxes to increase environmental quality.

Inference of stress-strength reliability for two-parameter of exponentiated Gumbel distribution based on lower record values.

The two-parameter of exponentiated Gumbel distribution is an important lifetime distribution in survival analysis. This paper investigates the estimation of the parameters of this distribution by using lower records values. The maximum likelihood estimator (MLE) procedure of the parameters is considered, and the Fisher information matrix of the unknown parameters is used to construct asymptotic confidence intervals. Bayes estimator of the parameters and the corresponding credible intervals are obtained by using the Gibbs sampling technique. Two real data set is provided to illustrate the proposed methods.

Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms.

Some countries have announced national benchmark rates, while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this study examines the determinants of TLREF. In this context, three global determinants, five country-level macroeconomic determinants, and the COVID-19 pandemic are considered by using daily data between December 28, 2018, and December 31, 2020, by performing machine learning algorithms and Ordinary Least Square. The empirical results show that (1) the most significant determinant is the amount of securities bought by Central Banks; (2) country-level macroeconomic factors have a higher impact whereas global factors are less important, and the pandemic does not have a significant effect; (3) Random Forest is the most accurate prediction model. Taking action by considering the study's findings can help support economic growth by achieving low-level benchmark rates.

The relationship between mobility and COVID-19 pandemic: Daily evidence from an emerging country by causality analysis.

This study examines the relationship between mobility (a proxy for transport) and the COVID-19 pandemic by focusing on Turkey as an example of an emerging country. In this context, eight types of mobility and two indicators of COVID-19 were analyzed using daily data from March 11, 2020 to December 7, 2020 by applying Toda-Yamamoto causality test. The findings revealed that (i) there is cointegration between the variables in the long term; (ii) there is an econometric causality between mobility indicators (mobility of grocery, park, residential, retail, and workplace) and pandemic indicators; (iii) various mobility indicators have an econometric causality with different pandemic indicators; (iv) neither driving mobility nor walking mobility has an econometric causality with the pandemic indicators whereas some of the other types of mobility, such as grocery, park, and retail do. These results generally show the effects of mobility and highlight the importance of appropriate mobility restrictions in terms of the pandemic.