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.

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.

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 and long-run impact of political stability on consumption-based carbon dioxide emissions in Finland: Evidence from nonlinear and Fourier-based approaches

The study investigates the asymmetric and long-run impact of political stability on consumption-based carbon dioxide (CCO₂) emissions in Finland. In this context, the study examines the impact of political stability, economic growth, renewable energy consumption, and trade openness; includes quarterly data between 1990/Q1 and 2019/Q4, and applies nonlinear and Fourier-based approaches. The empirical outcomes reveal that (i) there is a long-run cointegration between CCO₂ emissions and political stability as well as other controlling variables; (ii) positive changes in political stability have statistically significant impacts on CCO₂ emissions, whereas negative shocks in political stability are not statistically significant. Also, positive shocks are more powerful than negative shocks; (iii) positive shocks in economic growth have significantly increasing impacts; (iv) positive and negative shocks in renewable energy have decreasing impacts on CCO₂ emissions, while positive shocks are more powerful; (v) positive (negative) shocks in trade openness have decreasing (increasing) impacts on CCO₂ emissions. Overall, the empirical results highlight the role of political stability on CCO₂ emissions. Thus, consideration of political stability by policymakers of Finland in the policy development and implementation processes is highly recommended to achieve a carbon-neutrality target by 2035.

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.

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.