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Rising global oil prices are a major challenge for an emerging oil-importing nation such as Bangladesh. The majority of prior research on the economic effects of an oil price shock has concentrated on developed countries, with emerging economies receiving comparatively less attention. Bangladesh is vulnerable to price shocks due to its rising oil consumption over the past decade. This study aims to investigate how changes in oil prices would affect Bangladesh's total export earnings and to forecast the overall export volume. This study utilized a nonlinear autoregressive distributed lag (NARDL) approach to account for the asymmetric behavior of oil prices from 1991 to 2021. To assess the accuracy of predictions, the study employed the Prophet forecasting model and the Long Short-Term Memory (LSTM) method. Additionally, the symmetry test revealed a nonlinear relationship between export volume and oil price but a linear relationship between inflation and export volume. According to the NARDL assessment, both positive and negative oil shocks increase export earnings over the long run. The short run summary clarifies that both positive and negative changes in oil prices exert a significant negative effect on exports. Also, Inflation influences export earnings negatively in the short run but positively over the long term. Moreover, using machine learning methods, it was found that the LSTM method outperforms the prophet model in prediction performance with a low root mean square error (RMSE) of 1.88. Also, the analysis revealed policymakers that the export sector requires diversification to reduce its exposure to oil price shocks.

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This paper seeks to look into the asymmetric impacts posed by climate policy uncertainty (CPU) and investor sentiment (IS) upon the price of non-renewable energy, specifically natural gas prices, and the consumption of renewable energy, embodied in geothermal energy, biofuels, and fuel ethanol. To this end, the analysis draws on a non-linear autoregressive distributed lag (NARDL) model and wavelet coherence (WTC) technique with monthly data from January 2000 to December 2021. The NARDL results establish an asymmetric association between the variables, where negative shocks to CPU exert a greater effect on each energy variable than positive shocks, while the reverse is true for IS. Furthermore, it has been noticed that CPU and IS exhibit primarily negative correlations with the target variables over the long term, with CPU having a more pronounced effect on natural gas prices than on other forms of renewable energy consumption. Wavelet analysis also reveals that CPU leads the energy variables over the medium to long run, while IS assumes a dominant role in the short to medium run. These momentous findings underscore the importance of this study in informing energy policy formulation and environmental management, as well as optimizing investor portfolios.

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This article accounts for the impact of positive and negative shocks of the news-related Climate Policy Uncertainty (CPU) and the novel Economist Intelligence Unit's report-based global Energy Uncertainty (EU) on the U.S. sectoral stock returns by using the ARDL and NARDL approaches with dynamic multiplier simulations. We also utilize both the DCC-GARCH and ADCC-GARCH approaches to extract the symmetric and asymmetric dynamic conditional correlations between the EU and the U.S. sectoral stock returns and then regress these conditional correlation series on the CPU through series of quantile regressions. Overall, the findings suggest that only the positive CPU shocks negatively impact the U.S. sectoral stock returns of Consumer Services, Financials, Industries, Telecommunication and Utilities in the long-term, whereas the negative CPU shocks insignificantly predict the U.S. sectoral returns. The findings also report that only the negative EU shocks increase the U.S. sectoral stock returns of Consumer Services, Financials, Health Care, Industries, Moreover, the positive (negative) EU shocks cause the U.S. sectoral returns of Materials and Technology to decrease (increase) in the long-term. Portfolio managers may consider diversifying their portfolios to include sectors least susceptible to negative impacts from the CPU and EU shocks such as Health Care and Oil & Gas. Our findings also show that CPU shocks moderate the dynamic conditional correlations between the EU and the U.S. sectoral returns of Consumer Services, Materials, Health Care, Telecommunication, Oil and Gas and Utility. Fund managers should contemplate augmenting the allocations to the Financials, Industrials, and Technology sectors owing to their diminished interconnectivity with the EU during periods of heightened CPU.

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This study investigates the influence of economic policy uncertainty on climate change in selected African countries within asymmetric settings. Although previous research has examined the impact of various economic factors on climate change, the asymmetric effects of economic policy uncertainty have not been thoroughly explored, particularly in African countries. We analyze annual data spanning from 1980 to 2017 by utilizing three models: Panel Pooled Mean Group-Autoregressive distributed lag model (ARDL-PMG), Panel Pooled Mean Group-non-linear autoregressive distributed lag model (NARDL-PMG), and Dumitrescu-Hurlin asymmetric causality tests. According to the results of ARDL-PMG estimation, economic policy uncertainty has a detrimental impact on climate change in the long run. However, the NARDL-PMG estimation suggests that a positive shock in economic policy uncertainty negatively affects long-term climate change mitigation. However, a negative shock has a beneficial effect on climate change in the long term. In African nations, positive and negative changes in economic policy uncertainty failed to generate any significant climate change effects in the short run. The results also reveal that both positive and negative shocks in economic policy may cause climate change in a one-way direction. Based on the findings of our study, we recommend that African policymakers implement programs aimed at reducing economic policy uncertainties to help mitigate the effects of climate change.

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Asian countries are facing difficulties in attaining sustainable development goals (SDGs), and India is not an exception to it, with environmental degradation being one of the primary issues. Therefore, a policy-level reorientation may be required to address it. From this standpoint, fiscal policy instruments may come in handy towards fully integrating the SDGs into its agenda. The present investigation designs an SDG framework for India that could serve as an example for other Asian nations. This study introduces a new investigation exploring the relationship between fiscal policy instruments and environmental quality in India by examining the environmental Kuznets curve (EKC) hypothesis from 1990 to 2021. A nonlinear autoregressive distributed lag (NARDL) model is applied for empirical examination. The findings indicate that positive and negative shocks in fiscal policy instruments have significant impact on carbon emissions in both the long and short run. The study has also found evidence of an "inverted U-shape" EKC for India. These results are valuable from a policy perspective for India and other Asian countries to address environmental issues. The study has also outlined potential outcomes that may benefit India's fiscal policy in resolving environmental issues and attaining better economic growth. In the end, the study proposes a policy framework that supports SDG 7, SDG 8, SDG 12, SDG 13, and SDG 17 objectives.

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This study investigates the effects of environmental tax and environmental spending on CO2 emissions of 27 countries of the European Union EU27 countries using annual time series data from 1995 to 2022. The study used linear and non-linear autoregressive distributive lag (ARDL and NARDL) to examine the relationship. Estimates claim that the variables have a symmetric and asymmetric long-term and short-term relationship. The negative impacts of environmental taxes on CO2 emissions prove that emissions are reduced when polluting activities are taxed. Fiscal policy instrument such as taxation changes the behaviour of the private sector in the EU27 nations by disincentivizing polluting activities. On the other hand, government investment in environmental protection has encouraged the private sector in the EU27 nations to embrace and invest in green technologies, decreasing CO2 emissions. The ECM term is negative and statistically significant at a 1 percent significance level for ARDL and NARDL models, implying a stable long-run relationship between variables. It demonstrates that short-run disequilibrium converges to long-run equilibrium at a speed of 9.2% (in the ARDL model) and 22.7% (in the NARDL model). The study also sheds fresh light on the effectiveness of environmental taxes vs. expenditure, where taxes serve as a counter-incentive policy for CO2 emissions, and spending is a positive policy intervention.

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Green innovations are the most critical factor in promoting environmental sustainability worldwide. Trade can speed up the adoption of green innovations by facilitating the transfer of information, skills, and technology. However, trade policy uncertainty can create significant challenges for businesses investing in eco-innovations, leading to increased risk, reduced investment, and slower progress toward sustainable technologies. Recently, a growing number of researchers have shown their interest in finding the factors that can impact green innovations, but none have investigated the influence of trade policy uncertainty on green innovations in the USA and China. In addition, none of the past studies has relied on the nonlinear assumption. This analysis fills these gaps by examining the nonlinear impacts of trade policy uncertainty on eco-innovations in China and the USA over 2000Q1-2021Q4 by employing a nonlinear ARDL model. The finding reveals that a positive shock in trade policy uncertainty results in a decrease in green innovation in the USA and China, while a negative shock in trade policy uncertainty leads to an increase in green innovation in the USA over the long run. The nonlinear models also indicate that a positive shock in trade policy uncertainty harms green innovation in both the USA and China in the short run. The robustness of these results is confirmed by the NQARDL model, which confirms that an upsurge in trade policy uncertainty lowers green innovation in most quantiles in the USA and China in the short and long run. Conversely, negative shocks in trade policy uncertainty stimulate green innovation at most quantiles in both China and the USA, in the short and long run. Thus, policymakers need to consider the potential impact of trade policies on eco-innovations and work to create stable and predictable trade environments that support the growth of renewable technologies and other sustainable solutions.

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This study examines how patents on green technologies impact Algeria's ecological footprint from 1990 to 2022 while controlling for economic growth and energy consumption. The objectives are to analyze the asymmetric effects of positive and negative shocks in these drivers on ecological footprint and provide policy insights on leveraging innovations and growth while minimizing environmental harm. Given recent major structural shifts in Algeria's economy, time series data exhibits nonlinear dynamics. To accommodate this nonlinearity, the study employs an innovative nonlinear autoregressive distributed lag approach. The findings indicate that an upsurge in green technologies (termed as a positive shock) significantly reduces the ecological footprint, thereby enhancing ecological sustainability. Interestingly, a decline in green technologies (termed as a negative shock) also contributes to reducing the ecological footprint. This highlights the crucial role of clean technologies in mitigating ecological damage in both scenarios. Conversely, a positive shock in economic growth increases ecological footprint, underscoring the imperative for environmentally friendly policies in tandem with economic expansion. Negative shocks, however, have minimal impact. In a similar vein, positive shock in energy consumption increases ecological footprint, underlining the importance of transitioning towards cleaner energy sources. Negative shock has a smaller but still noticeable effect. The results confirm asymmetric impacts, with positive and negative changes in the drivers affecting Algeria's ecological footprint differently. To ensure long-term economic and ecological stability, Algeria should prioritize eco-innovation and green technology development. This will reduce dependence on fossil fuels and create new, sustainable industries.

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R&D spending upsurges technological advancement and innovation which results in lowering energy consumption and environmental degradation. The current study investigates the asymmetrical impact of R&D spending on CO2 emissions in China via employing annual data from 1980 to 2021 and the NARDL model for empirical analysis. The estimated results of the NARDL model confirmed that there are asymmetries in positive and negative coefficients of R&D spending in China. The results depict that the positive shock in R&D spending exerts a negative and statistically significant impact on CO2 emissions in both runs implying that an increase in R&D spending lowers CO2 emissions. However, the negative coefficient of R&D spending yields a positive and statistically significant impact on CO2 emissions revealing the fact that a negative shock in R&D spending results in the upsurge of CO2 emissions in China. According to these findings, the impact of positive and negative shocks in R&D spending on CO2 emissions is asymmetric. The findings also show that the impact of a negative shock in R&D spending is greater than the impact of a positive shock on CO2 emissions. In addition to the negative shock in R&D spending, increases in energy consumption, economic growth, and FDI inflows also contribute to an upsurge in CO2 emissions in China. The robustness of the estimated results is assessed using standard fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) models. The FMOLS and DOLS results have been confirmed to be sound and consistent with the results of the NARDL model. The study suggests that the economic strategies should aim at investing in R&D spending to foster environment-friendly technological innovations and to lower environmental degradation in China.

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The novelty of this study is to examine the asymmetric effect of the exchange rate on bilateral export and import between Bangladesh and its three trading partners in the Bangladesh-China-India-Myanmar Economic Corridor using nonlinear ARDL models from 1973 to 2022. After controlling income and structural breaks, the empirical findings confirm the asymmetric effects of exchange rates on the short-run and long-run export and import demand functions of Bangladesh. Furthermore, the impacts of the appreciation and depreciation of the Bangladeshi currency are heterogeneous for these three trading partners. For instance, the depreciation of the Bangladeshi currency increases exports to China and India while it decreases exports to Myanmar in the short run. However, the depreciation increases exports to India and Myanmar, and the appreciation also increases exports to China and India in the long-run. On the contrary, depreciation increases imports from China and Myanmar in the short-run, while it decreases imports from Myanmar in the long run. Only appreciation has significant negative effects on China and India. As a robustness measure, we exclude the COVID-19 period. However, it does not substantially change our main findings.

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Estimating the asymmetrical influence of foreign direct investment is the primary goal of the current study. In addition, further controlled variables affect environmental degradation in OIC nations. Due to this, current research employs the asymmetric (NPARDL) approach and the data period from 1980 to 2021 to estimate about viability of the EKC (environmental Kuznets curve) theory. The study utilized greenhouse gas (GHG) including emissions of carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and ecological footprint as substantial parameters of environmental quality. A nonlinear link between foreign direct investments, trade openness, economic growth, urbanization, energy consumption, and environmental pollution with CO2, N2O, CH4, and ecological footprint in the OIC nations is confirmed by the study's outcomes, which however reveals inconsistent results. Furthermore, the results also show that wrong conclusions might result from disregarding intrinsic nonlinearities. The study's conclusions provide the most important recommendations for decision-makers.

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The objective of this study is to scrutinize the impact of low carbon energy consumption, environmental-related technological innovation, urbanization, economic growth, and trade on China's ecological footprint from 1980 to 2021. To investigate the nature of the long-term connections between the variables, we employ the symmetric and asymmetric autoregressive distributed lag (ARDL) technique to explore the long- and short-run elasticities of coefficients. The results of ARDL and NARDL verified the environmental Kuznets curve (EKC) hypothesis in the long run in the presence of low carbon energy consumption. Moreover, the findings show that the positive part of low carbon energy consumption is negatively connected with the ecological footprint. Alternatively, the positive part of low carbon energy consumption is positively linked with the ecological footprint. The outcome highlights that environment-related technological innovation reduces the level of ecological footprint. Similarly, urbanization has a detrimental effect on the ecological footprint. Based on the estimated findings, it is suggested that China's economy should place a greater emphasis on increasing its level of investment in the low carbon energy sector and adopting severe environmental legislation to protect the economy from environmental burden.

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Increasing industrial activities trigger the intense use of fossil fuels and increase the number of carbon emissions in the atmosphere. Countries with a high share in current carbon emissions need to expand their use of renewable energy sources. Canada is an important energy producer and consumer globally. In this regard, its decisions are important for the future development of global emissions. This study examines the asymmetric effects of economic growth, renewable energy, and non-renewable energy consumption on carbon emissions in Canada from 1965 to 2017. In the first stage of the analysis, unit root testing was performed for the variables. For this, Lee-Strazicich (2003), ADF and PP unit root tests were used. The nonlinear ARDL method was used to analyze the relationship between variables. and Measures: In order to analyze the relationship between the variables in the established model, renewable energy consumption (%), non-renewable energy consumption (%), and carbon emissions (per capita-Mt). In addition, the economic growth (constant price 2010- US$) parameter was added to the model as a control variable. The findings support that energy consumption, economic growth, and renewable energy have an asymmetric effect on carbon emissions in the long run. The positive shock in renewable energy reduces carbon emissions, and a unit increase in renewable energy reduces carbon emissions by 1.29%. Besides, the negative shock in economic growth greatly deteriorates the quality of the environment; that is, a 1% reduction in economic growth causes emissions to increase by 0.74% in the long run. On the other hand, positive shocks in energy consumption have a positive and significant effect on carbon emissions. A 1% increase in energy consumption causes 1.69% carbon emissions. There are important policy implications for Canada to eliminate carbon emissions, increase the share of renewable energy sources and achieve its economic growth targets. In addition, Canada needs to reduce its consumption of non-renewable energy (such as gasoline coal, diesel, and natural gas).

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This study explores the correlation between microfinance loans (MFL) and economic growth in Bosnia and Herzegovina (Bosnia). It utilizes the non-linear Autoregressive Distributed Lag (NARDL) method to examine cointegration and short-run dynamics by analyzing quarterly data spanning from 2010 to 2022. The findings underscore the link between MFL shocks and long-term economic growth. The study unveils the unique effects of both positive and negative MFL shocks on growth, suggesting a non-linear relationship between microfinance loans and economic growth in Bosnia. However, the study concludes that the impact of MFL on Bosnia's GDP is adverse. Short-term fluctuations in MFL show no substantial influence on Bosnian economic growth. The coefficient of the error correction model is both negative and significant indicating the stability of the long-term relationship. This implies a rapid correction, with 46.4 % of the previous quarter's imbalance rectified within the current quarter. While our results are based on a single country, they align with recent criticisms of microfinance practices. Furthermore, our study offers a novel approach as it represents the first examination of the asymmetric relationship between MFL and GDP in Bosnia, providing valuable policy recommendations.

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Considering the substantial environmental and economic consequences, countries worldwide have placed renewable energy at the forefront of their priorities. While previous empirical research has extensively explored the link between renewable energy and economic growth, this study seeks to address an overlooked aspect by investigating the potential impact of a particular indicator on economic activity. This paper examines the relationship between current account balance and renewable energy in OECD countries using an autoregressive distributive lags (ARDL) model to explore symmetric relationships and the non-linear autoregressive distributive lag model (NARDL) approach and panel vector autoregression (P-VAR) model over the period 1995-2020. The results indicate that there is a cointegrating relationship and that renewable energy consumption has a positive impact on the current account balance in the long term, but no significant impact in the short term. Given that many OECD countries are struggling with deficits, prioritizing bioenergy trade would be beneficial to increase the current account surplus.

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This research work contributes to the literature by examining the role of energy consumption in mitigating poverty via decomposing energy consumption into its positive and negative components, covering the period spanning from 1985 to 2017. To accomplish this objective, this study employs the nonlinear autoregressive distributed lag (NARDL) approach recently popularized by Shin et al., (2014). The NARDL approach is well-suited to our study becuase of its capability to delineate hidden asymmetries. The empirical findings reveal the prevalence of long-run associations among the studied variables. The outcomes show that an increase (decrease) in energy consumption combats (augments) poverty in Pakistan. The empirical findings underscore that the decreasing effect of energy consumption on poverty is found to be more promising than its increasing effect, both in the long and short run. Based on the empirical outcomes, we suggest that the policymakers, and other stakeholders should consider the asymmetric or nonlinear behavior of the studied variables for better poverty policy-making in Pakistan.

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The majority of South Africa's population lives in the same economy as poverty, even though the country's first democratic elections in 1994 not only lit a candle of hope but also helped to abolish poverty. One of the main hurdles to reducing poverty is economic growth, while unemployment is one of the mutual friends with poverty. Therefore, in this study, unemployment and economic growth were included as explanatory variables, while poverty was used as the dependent variable. To understand how unemployment and economic growth affect poverty, Autoregressive Distributed Lags (ARDL) and non-linear Autoregressive Distributed Lags (NARDL) models were used through the time series data from 2000Q1 to 2021Q4. Based on linear evidence, the findings of the study supported the idea that economic growth reduces poverty in the long-run, while unemployment inflates poverty in the long-run. The asymmetric evidence confirmed that although negative shocks of economic growth reduce the poverty rate, the positive shocks of the former reduce the poverty rate. On the other hand, poverty rates rise concurrently as a result of both positive and negative shocks of unemployment rates. Thus, it is advised that policymakers increase social investment to help urban and rural residents, particularly women and children, escape poverty.

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The focus of sustainable development goals (SDGs) is to promote the use of renewable energy so that countries can achieve better environmental quality. However, the progression is plodding, and still, 80% of energy comes mainly from conventional sources in developing countries. The implementation of procedures depends on the political attitudes, political stability, and quality of institutions. India has a diverse political structure ranging from central government to state government to local governments. In the late '80 s, India witnessed a stiff rise in regional and national political parties, which leads more political competition. This paper tries to explain the possible relationship between political competition and CO2 emission in India. With the application of the time series non-linear ARDL (NARDL) model, this study tries to find the asymmetric relationship between political competition and CO2 emission. In our empirical model, we also include other important elements of environmental quality like innovation and fossil fuel consumption. Empirical results show that political competition is asymmetrically related to CO2 emissions in the long run. Fossil fuel consumption and innovation also have a significant relationship with emissions. Based on the results, a few policy recommendations have been discussed.

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This paper estimates and establishes the causality between the Human Development Index (HDI), Gross Domestic Product (GDP), inflation and CO2 emissions on crude oil production (COP) in Cameroon from 1977 to 2019. To do so, the Augmented Dicky-Fuller and Zivot-Andrews stationarity tests, ARDL and NARDL modelling, as well as Toda-Yamamoto causality test are performed. Unlike previous studies on COP, this study incorporates the asymmetric impact (NARDL). The results indicate that CO2 emissions and GDP have a negative impact on COP in the long-run, while HDI and inflation have a positive impact in the short-run. GDP and HDI have a non-linear impact in the short run, while in the long-run inflation and CO2 emissions have a non-linear impact on COP. From these results, it is interesting to note that, in order to allow future generations to benefit from the oil windfall. The diversification of the Cameroonian economy, the control of inflation and the use of less polluting crude oil extraction technologies must be imperative.•A step-by-step procedure of the ARDL, NARDL and causality test is provided.•The multiplier effects of GDP, HDI, inflation and CO2 emissions on COP are simulated.•The impact of GDP and HDI on COP is non-linear.

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The BRICS nations-Brazil, Russia, India, China, and South Africa-have grown significantly in importance over the past few decades, playing a vital role in the development and growth of the global economy. This expansion has not been without cost, either, since these countries' concern over environmental deterioration has risen sharply. Both researchers and decision-makers have focused a lot of attention on the connection between economic growth and ecological sustainability. By using nonlinear autoregressive distributed lag (NARDL) approach, the complex relationships were analyzed between important economic indicators-such as gross domestic product (GDP), ecological innovations (EI), energy consumption (ENC), institutional performance (IP), and trade openness (TOP)-and their effect on carbon emissions and nitrous oxide emissions in the BRICS countries from 1990 to 2021, this study seeks to contribute to this important dialog. Principal component analysis is formed for technological innovations and institutional performance using six (ICT service exports as a percentage of service exports, computer communications as a percentage of commercial service exports, fixed telephone subscriptions per 100 people, internet users as a percentage of the population, number of patent applications, and R&D expenditures as a percentage of GDP) and twelve (government stability, investment profile, socioeconomic conditions, internal conflict, external conflict, military in politics, control of corruption, religious tensions, ethnic tensions, law and order, bureaucracy quality, and democratic accountability) distinct indicators, respectively. The results of nonlinear autoregressive distributed lag estimation show that increase in economic growth would increase carbon dioxide and nitrous oxide emissions. The positive and negative shocks in trade openness have positive and significant impact on carbon dioxide and nitrous oxide emissions in BRICS countries. Furthermore, the positive shock energy consumptions have positive and significant effect on Brazil and India when carbon dioxide and nitrous oxide emissions are used. However, EKC exists in BRICS countries when carbon dioxide and nitrous oxide emissions are used. According to long-term estimation, energy consumption and technological innovations in the BRICS countries show a strong and adverse link with nitrous oxide and a favorable relationship with carbon dioxide emissions. In the long run, environmental indicators are seen to have a major and unfavorable impact in BRICS nations. Finally, it is proposed that BRICS nations can assure environmental sustainability if they support creative activities, enhance their institutions, and support free trade policies.

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