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Pursuing green growth is imperative to cope with the climate change battle. Green growth in top-polluting economies is being encouraged. The underlying work is aiming to investigate the impact of environmental technology and banking sector on green growth. More precisely, the study employs CS-ARDL and PMG-ARDL methods for empirical assessment. The FMOLS and DOLS techniques have been used to perform the sensitivity analysis for CS-ARDL and PMG-ARDL results. Empirical evidence of both the CS-ARDL and PMG-ARDL models reveals that banking sector development and environmental technology promote green growth. In detail, the insights reveal the significant and positive effect of environmental innovations and technology on green growth in both long-run as well as in short-run. Moreover, the findings of the study also disclose the significant and positive effect of banking sector and stock market developments on green growth in both long-run and short-run. Sensitivity analysis confirmed and improved our findings. Based on these effects, the study delivers policy implications for the promotion of environmental-based technological innovations and financial sector development to enhance green growth in top-polluted economies.

This paper seeks to investigate the short-run and long-run relationship between price level and monetary, non-monetary and external factors of inflation in Uzbekistan, from January 2016 to August 2025. The Autoregressive Distributed Lag (ARDL) model is applied to examine the link between money (M), interest rate (R), exchange rate (FX), producer price index (PP), import volume (IM), global food prices and the consumer price index (CPI). The estimates of the bound test found the cointegrating associations among the variables. The alternative Johansen test confirmed the existence of such a long-run relationship. Additionally, M, FX and PP have long-run relationships with the CPI, while the elasticity of the CPI with respect to R and IM is not statistically significant in the long run. The error correction term (ECM) has a value of –0.18, which means that 18% of the deviation is corrected in each month. Moreover, R and FX have a positive and statistically significant effect on the CPI in the short run. Finally, the Toda and Yamamoto causality test established the bidirectional causality between M and the CPI in Uzbekistan.

The COVID-19 pandemic storm has struck the world economies and energy markets with extreme strength. The goal of our study is to assess how the pandemic has influenced oil and gas prices, using energy market reactions in the United States and Japan. To investigate the impact of the COVID-19 cases on the crude oil and natural gas markets, we applied the Auto-Regressive Distributive Lag (ARDL) approach to the number of the US and Japanese COVID-19 cases and energy prices. Our study period is from 21 January 2020 to 2 June 2020, and uses the latest data available at the time of model calibration and captures the so-called “first pandemic wave”. In the US, the COVID-19 pandemic had a statistically negative impact on the crude oil price while it positively affected the gas price. In Japan, this negative impact was only apparent in the crude oil market with a two-day lag. Possible explanations of the results may include differences in pandemic development in the US and Japan, and the diverse roles both countries have in energy markets.

Purpose While several existing panel studies have focused on the linear specifications of the effect of remittances and financial development on carbon emissions, nonlinear panel studies on this subject remain thin on the ground. The purpose of this paper is to examine the asymmetric effect of remittances and financial development on carbon emissions in 31 selected sub-Saharan African countries for the period spanning from 1996 to 2018. Design/methodology/approach The Kao, Pedroni and Johansen–Fisher co-integration tests were conducted to ascertain a long-run relationship among the studied variables, whereas the nonlinear panel autoregressive distributed lag approach was applied to account for asymmetries. Findings The study revealed, among other things, that remittances and financial development asymmetrically influence carbon emissions in the selected panel of sub-Saharan African countries. In the long run, the positive shock in remittances on carbon emissions is greater than in the negative shock in remittances. Additionally, both positive and negative shocks in financial development mitigate carbon emissions. Research limitations/implications The implications of this study include the need to provide tax incentives to remitters and encourage them to invest in clean technologies so as to maintain sustainable development and low carbon emissions in the environment. There is also the need for governments and policymakers to formulate policies aimed at improving the functioning of the financial sectors in sub-Saharan Africa. Originality/value The positive and negative shocks of remittances and financial development on carbon emissions are examined to ascertain their asymmetric relationships.

In the contemporary world, environmental degradation has become a concern for human beings. Accordingly, the impact of social welfare, economic policy uncertainty, natural resource rents, life expectancy, and trade openness are examined on ecological footprint (the most comprehensive proxy of environmental degradation) in 19 energy-intensive countries from 1997 to 2018. With this in mind, this study used the traditional panel ARDL and CS-ARDL approaches to evaluate how the study’s variables influence ecological footprint. Notably, the results of the CS-ARDL approach are more robust due to cross-sectional dependence and slope heterogeneity problems. The outcomes revealed that economic policy uncertainty and trade openness affect the ecological footprint negatively in the short run and positively in the long run. Moreover, social welfare degrades the environment in the long run, and natural resource rents improve environmental quality by mitigating the ecological footprint in the short run and harming the environment in the long run. Besides, life expectancy does not significantly affect ecological footprint in the long or short run. Meanwhile, the results confirmed the bi-directional causal relationship between the study’s variable and ecological footprint. Based on the outcomes, the way to adopt effective policies to improve the quality of the environment has been paved. Furthermore, a comprehensive policy framework for stricter environmental regulation is expected to be developed using the outcomes derived from this study.

Purpose: This study aimed to examine the relationship between savings and investment in the Jordanian economy during the period (1980-2020).   Design/Methodology/Approach: This study was done using Augmented Dicky Fuller and Phillips-Perron unit root tests, and Lumsdaine and Papell unit root tests with structural breaks to determine if the time-series variables are stable or not. The Autoregressive Distributed Lag (ARDL) Bounds test method was used in this study to test long-run relationship between savings and investment   Findings: The findings of the Bounds test suggest a term savings-investment relationship. This outcome is consistent with a number of recent research reviewed in the literature that have shown that saving and investment are co-integrated in the long term.   Practical implications: keeping a sustainable supply of savings should be a top policy goal for economic stability, which can assist policymakers and institutions in selecting their future actions.

Concerns regarding environmental sustainability have generally been an important element in achieving long-term development objectives. However, developing countries struggle to deal with these concerns, which all require specific treatment. As a result, this study explores the interaction between financial development, renewable energy consumption, technological innovations, and CO2 emissions in India from 1980 to 2019, taking into account the critical role of economic progress and urbanization. The Autoregressive Distributed Lag (ARDL) model is used to quantify long-run dynamics, while the Vector Error Correction Model is used to identify causal direction (VECM). According to the study’s conclusions, financial development has a considerable positive impact on CO2 emissions. The coefficient of renewable energy consumption and technical innovations, on the other hand, is strongly negative in both the short and long run, indicating that increasing these measures will reduce CO2 emissions. Furthermore, economic expansion and urbanization have a negative impact on environmental quality since they emit a significant amount of CO2 into the atmosphere. The results of the robustness checks were obtained using the Fully Modified Ordinary Least Squares (FMOLS), the Dynamic Ordinary Least Squares (DOLS), and the Canonical Cointegration Regression (CCR) approaches to verify the findings. The VECM results reveal that there is long-run causality in CO2 emissions, financial development, renewable energy utilization, and urbanization. A range of diagnostic tests were also used to confirm the validity and reliability. This study delivers new findings that contribute to the existing literature and may be of particular interest to the country’s policymakers in light of the financial system and its role in environmental issues.

Globalization has contributed to several advances in technology including linking people around the globe and driving us to modern economies. With fast economic growth and industrialization progress, the negative impact of globalization on biodiversity can be easily ignored. Globalization is an undeniable factor in our planetary devastation from pollution to global warming and climate change. The major intention of our recent analysis was to examine the globalization, energy consumption, trade, economic growth, and fuel importation to determine the ecological footprint in Pakistan by taking the annual data variables from 1974–2017. A linear ARDL (autoregressive distributed lag) technique with limited information maximum likelihood and linear Gaussian model estimation were utilized to check the variables association. Outcomes show that in the long run, globalization, energy usage, trade, and GDP growth have consistently productive interactions with the ecological footprint, while an examination of fuel importation uncovers an adversative linkage to impacts on the ecological footprint in Pakistan. Similarly, the findings of short-run interactions also reveal that globalization, energy usage, trade, and GDP growth have constructive linkages; however, an examination of fuel importation also uncovers an adversative linkage to impacts on the ecological footprint. The outcomes of limited information maximum likelihood also expose that the variables of globalization, energy usage, trade, and fuel importation have productive linkages, while an examination the GDP growth uncovers an adversative linkage to the ecological footprint. Furthermore, the outcomes of the linear Gaussian model estimation also uncover that globalization and energy usage demonstrate a constructive linkage, while other variables reveal an adverse linkage to the ecological footprint. Environmental pollution is now an emerging issue which causes the climatic variations associated with greenhouse gases emissions. The Pakistani government must adopt new strategies to ensure that CO2 emissions are reduced in order to stimulate economic growth.