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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., Competing Interests: Declarations. Competing interest: The authors declare no conflict of interest. Ethics Approval and Consent to Participate: Not applicable. Consent for Publication: The authors are willing to permit the Journal to publish the article., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 (CO 2 ) 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 CO 2 emissions; (ii) renewable energy and hydroelectricity consumption have a decreasing impact, whereas natural gas, coal, and oil energy consumption have an increasing impact on CO 2 emissions; (iii) although nuclear energy has been discussed as a potential alternative, nuclear energy does not have a significant impact in decreasing CO 2 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 CO 2 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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study deals with the asymmetric effect of economic policy uncertainty and political stability on carbon dioxide (CO 2 ) 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 CO 2 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 CO 2 emissions; (iii) energy consumption has an accelerating effect on CO 2 emissions while the power of effect changes at quantiles; (iv) economic growth has generally an increasing effect on CO 2 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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 (CO 2 ) 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 CO 2 emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R 2 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 CO 2 emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO 2 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 CO 2 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 CO 2 emissions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 (CO 2 ) 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 CO 2 emissions. The results reveal that (i) political stability has a statistically significant effect on production-based CO 2 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 CO 2 emissions; and (iii) there is frequency domain causality from political risk, economic growth, renewable energy consumption, and trade openness to production-based CO 2 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 CO 2 emissions in the long run., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)