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.

How are electricity generation effective on carbon neutrality in the global south? Evidence from sectoral CO2 emissions by daily data.

In light of the efforts to ensure carbon neutrality by combating climate-related problems, the study investigates the effectiveness of electricity generation (EG) from the main renewable sources (hydro-HEG, solar-SEG, and wind-WEG). In this context, the study examines the countries of the Global South (i.e., Brazil, China, and India), considers EG at a disaggregated level and sectoral CO2 emissions, applies nonlinear methods, and uses daily data between January 2, 2019 and December 31, 2022. The results demonstrate that (i) disaggregated EG sources have a stronger (weaker) time and frequency dependency on sectoral CO2 emissions in China (Brazil and India); (ii) HEG has a stimulating impact on sectoral CO2 emissions in all countries; (iii) SEG has an increasing impact on sectoral CO2 emissions in Brazil and China, while it provides a decrease in sectoral CO2 emissions in India; (iv) WEG upsurges sectoral CO2 emissions in China, while it achieves a CO2 reduction in Brazil and India; (v) disaggregated level EG has a causal impact on sectoral CO2 emissions across all quantiles except some lower, middle, and higher quantiles. The study adds scientific value to existing knowledge by analyzing for the first time which EG sources are effective in reducing daily CO2 emissions in the Global South. Based on the outcomes, the study demonstrates that WEG is the best EG source for Brazil, that SEG and WEG are optimal EG sources for India, and that China cannot benefit from the EG sources considered. In this way, the study provides fresh insights for the countries of the Global South and underlines the crucial role of renewable EG in ensuring carbon neutrality.

Quantile-based effect of energy, transport, and total environmental tax on ecological footprint in EU5 countries.

Considering a vast majority of application areas, the study investigates how environmental tax (ET) affects ecological footprint. In this context, the study examines the European Union Five (EU5) countries, considers ecological footprint (EF) as the proxy of the environment, uses ET as tax-based environmental measures by making both disaggregated (i.e., energy and transport) and aggregated level analysis, and performs novel nonlinear quantile-based approaches for the period from 1995/Q1 to 2021/Q4. The outcomes show that on EF (i) energy-related ET has only a declining effect at lower and middle quantiles in Germany and at lower quantiles in Italy, whereas it does not have a curbing effect in other countries; (ii) transport-related ET is not effective on EF in any country, which means that it does not have a curbing effect; (iii) total ET has a decreasing effect in only Germany; and (iv) the alternative method validates the robustness. Thus, the study demonstrates the changing effect of ET across countries, quantiles, and ET types in curbing EF. Hence, it can be suggested that Germany can go on relying further on energy-related ET practices to decrease EF, whereas there is a long way for the remaining EU5 countries as well as transport-related ET in curbing EF.

Time and frequency analysis of daily-based nexus between global CO2 emissions and electricity generation nexus by novel WLMC approach.

The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world.

Does Climate Change Cause an Upsurge in Food Prices?

Climate change is the reason behind most contemporary economic problems. The rising inflationary pressures in the food sector are one of these problems, and stable food prices are a necessity for economic development and social cohesion in societies. Therefore, this study analyzes the relationship between food prices and climate change in Nigeria by using various non-linear and quantile-based methods and data from 2008m5 to 2020m12. The empirical findings indicate that (i) there is a time- and frequency-based dependence between food prices and some explanatory variables, including climate change (i.e., temperature). (ii) At higher quantiles, temperature, oil prices, food exports, monetary expansion, global food prices, agricultural prices, and fertilizer prices stimulate food prices. (iii) The increase in food prices due to the rise in temperature and the difficulties in agriculture indicate that the heatflation phenomenon is present in Nigeria. The evidence outlines that Nigerian decisionmakers should adopt a national food security policy that considers environmental, agricultural, and monetary factors to stabilize food prices.

Impact of renewable energy investments in curbing sectoral CO2 emissions: evidence from China by nonlinear quantile approaches.

The study analyzes the impact of renewable energy investments (RENIV) on the environment in China. In doing so, the study uses sectoral carbon dioxide (CO2) emissions as the environment indicator, considers RENIV as the explanatory variable, includes monthly data from 2004/1 to 2020/6, runs quantile on quantile regression approach as the fundamental model, and further performs quantile regression for the controlling. The study reveals that RENIV curb CO2 emissions in all sectors at higher levels of sectoral CO2 emissions. Also, RENIV have a varying impact based on quantiles and sectors. Moreover, the results are robust based on the alternative approach. Thus, RENIV have a significantly decreasing impact on sectoral CO2 emissions in China. Accordingly, China policymakers should continue to focus on providing a decrease in energy and industrial sector CO2 emissions as the highest emitting sectors.

Do life expectancy and hydropower consumption affect ecological footprint? Evidence from novel augmented and dynamic ARDL approaches.

Human activities threaten the future of the ecosystem by emitting pollution to the air, water, and soil. Considering the increasing ecological footprint (EF), the study focuses on investigating the role of life expectancy and hydropower consumption by controlling also income, trade openness, and globalization on the environment under the environmental Kuznets curve (EKC) hypothesis for Turkey during 1971-2018. In this context, the study performs recently developed augmented autoregressive distributed lag (AARDL) and dynamic ARDL (DARDL) methods. The results show that (i) life expectancy increases the environmental pressure; (ii) hydropower consumption has no effect on the EF; (iii) globalization and trade openness reduce the EF; (iv) the EKC hypothesis is valid, but the estimated turning point lies between USD 19,914 and USD 20,571, which is far from the sample period in Turkey. From the overall results, it can be concluded that Turkey cannot solve environmental problems with insufficient income levels, an increasing elderly population, and ineffective use of hydropower. Hence, Turkey should rely on income much more, use hydropower much more efficiently, and benefit from the spillover effect of technological innovations related to globalization and foreign trade to significantly reduce the EF.

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.

Environmental reverberations of geopolitical risk and economic policy uncertainty resulting from the Russia-Ukraine conflict: A wavelet based approach for sectoral CO2 emissions.

After the COVID-19 pandemic, Russia invaded Ukraine in February 2022, and a natural gas crisis between the European Union (EU) and Russia has begun. These events have negatively affected humanity and resulted in economic and environmental consequences. Against this background, this study examines the impact of geopolitical risk (GPR) and economic policy uncertainty (EPU) caused by the Russia-Ukraine conflict, on sectoral carbon dioxide (CO2) emissions. To this end, the study analyzes data from January 1997 to October 2022 by using wavelet transform coherence (WTC) and time-varying wavelet causality test (TVWCT) approaches. The WTC results show that GPR and EPU reduce CO2 emissions in the residential, commercial, industrial, and electricity sectors, while GPR increases CO2 emissions in the transportation sector during the period from January 2019 to October 2022, which includes Russia-Ukraine conflict. The WTC analysis also indicates that the reduction in CO2 emissions provided by the EPU is higher than that of the GPR for several periods. According to the TVWCT, there are causal impacts of the GPR and the EPU on sectoral CO2 emissions, but the timing of the causal impacts differs between the raw and decomposed data. The results suggest that the EPU has a larger impact on reducing sectoral CO2 emissions during the Ukraine-Russia crisis and that production disruptions due to uncertainty have the greatest impact on reducing CO2 emissions in the electric power and transportation sectors.

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.

Analyzing the co-movement between CO2 emissions and disaggregated nonrenewable and renewable energy consumption in BRICS: evidence through the lens of wavelet coherence.

This study investigates the time-frequency nexus of carbon dioxide (CO2) emissions with economic growth, nonrenewable (i.e., coal, natural gas, and oil), and renewable (i.e., hydro and geothermal) energy consumption. In this context, BRICS countries (namely, Brazil, Russian Federation, India, China, and South Africa), which are leading emerging countries, are included, and quarterly data from 1990/Q1 to 2019/Q4 is used. The study employs the wavelet coherence (WC) approach to explore the co-movement between the variables at different frequencies. The empirical results show that (i) there is a strong and positive co-movement between CO2 emission and economic growth; however, it is weak for Russia and South Africa in the medium and long-term; (ii) coal energy consumption is strongly and positively co-moved with CO2 emission for all BRICS countries; (iii) natural gas energy consumption is strongly and positively co-moved with CO2 emissions in Brazil, India, and China; however, it is weakly and positively co-moved in Russia and South Africa; (iv) oil energy consumption is strongly and positively co-moved with CO2 emissions in Brazil, India, and China; however, it changes a bit for Russia and South Africa; (v) hydro energy consumption is weakly and positively co-moved with CO2 emissions in general, whereas country-based results vary; (vi) geothermal energy consumption is also similar to hydro energy consumption. Thus, the WC results highlight the strong co-movement of economic growth and nonrenewable energy consumption with CO2 emissions, whereas renewable energy consumption has a relatively lower co-movement. Based on the results, policy implications are also discussed for BRICS countries.

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.

Role of hydroelectricity and natural gas consumption on environmental sustainability in the United States: Evidence from novel time-frequency approaches.

This study analyzes time- and frequency-varying impacts of hydroelectricity energy consumption, natural gas energy consumption, and economic growth on environmental sustainability proxied by carbon dioxide (CO2) emissions in the United States of America (the US) for the period 1965/Q1 to 2020/Q4. This study is the first of its kind to contribute to the current literature by analyzing dynamic relationships among these variables in the short-, medium-, and long-term at different time frequencies in the framework of a multivariate correlation, hence providing a more comprehensive picture about the impacts of these effective factors on CO2 emissions. To meet the objectives of the study, Wavelet local multiple local (WLMC), which is a recent novel methodology developed by Polanco-Martínez et al. (2020), is applied. Moreover, the Wavelet coherence (WTC) approach is used for robustness check. The outcomes provide fresh insights into the long-term dynamic correlations among hydroelectricity energy consumption, natural gas energy consumption, economic growth, and CO2 emissions. The study discovers a robust positive co-movement between natural gas energy consumption and CO2 emissions and a negative correlation between hydro energy consumption and CO2 emissions that is the most intense on the long-term frequencies. Furthermore, economic growth causes CO2 emissions, which is evidenced by a positive relationship between both factors at short- and long-term time-frequencies. Supported by the outcomes of the study, the authors urge to suggest crucial insights and policy points for the US policymakers to shift from fossil energy to renewable energy sources to meet Sustainable Development Goals (SDGs), especially SDG-7 and SDG-13, since they induce lower emissions.

Role of country risks and renewable energy consumption on environmental quality: Evidence from MINT countries.

This study focuses on uncovering the effect of country risks and renewable energy consumption on environmental quality. In this context, the study examines Mexico, Indonesia, Nigeria, and Turkey (MINT) nations; takes economic growth, trade openness, and urbanization into account; includes data from 1990 to 2018; applies cross-sectional autoregressive distributed lag (CS-ARDL) as the main model while common correlated effects mean group (CCEMG) and augmented mean group (AMG) for robustness checks. The empirical results show that (i) economic growth, political risk, urbanization, and trade openness contribute to an increase in ecological footprint; (ii) economic and financial risks as well as renewable energy use have a positive influence on environmental quality; (iii) a unidirectional causality exists from economic risk, financial risk, political risk, economic growth, urbanization, and trade openness to the ecological footprint: (iv) the validity of the EKC hypothesis for the MINT economies is verified; (v) the robustness of CS-ARDL results are validated by CCEMG and AMG approaches. Based on these results, policymakers should promote a sustainable environment to lessen the ecological footprint. Additionally, governments should firmly support investments in green technology as well as economic and financial stability to boost energy efficiency and promote the adoption and usage of energy-saving products.