Repercussions of Hydroelectricity use on Carbon Emissions in Bangladesh: Evidence using Novel Fourier-Bootstrapped ARDL and Fourier-Gradual Shift Causality analyses

Comparative analysis of hydro energy determinants for European Economies using Golden Cut-oriented Quantum Spherical fuzzy modelling and causality analysis

This article presents a comparative analysis of the determinants of hydropower for European economies using Golden Cut oriented Quantum Spherical fuzzy modelling and causality analysis in 24 European countries over the period 2001-2020. The indicators chosen for the analysis are inflation, population, GDP per capita, CO2 and hydropower consumption. The analysis shows that the selected groups of countries are characterised by an inverse relationship between GDP per capita and hydropower consumption, suggesting a bi-directional causal relationship, which also confirms the novelty of this paper. Furthermore, another analysis is carried out using the fuzzy decision-making methodology. In this framework, the directions of influence of the five selected indicators are constructed: GDP per capita (criterion 1, D = 88.656, E = 88.083), hydropower consumption (criterion 2, D = 89.471, E = 88.677), population (criterion 3, D = 87.705, E = 89.228), CO2 emissions (criterion 4, D = 88.578, E = 89.186) and inflation (criterion 5, D = 88.943, E = 88.180). The Quantum Spherical fuzzy methodology is used for this purpose. The values of D and E are measures of the sum of the rows and columns of the overall relationship matrix. Hydropower consumption is the main criterion. It is understood that two different analyses give similar results, namely the bidirectional causal relationship between criteria 1 and 2.

Achieving SDG-13 in the Era of Conflicts: The Roles of Economic Growth and Government Stability

Establishing effective climate control and reducing the ecological footprint (EF) are necessary for pursuing Sustainable Development Goals (SDGs), in particular Goal 13. In this context, it is required to enhance the understanding of various factors that can either decrease or enhance the EF. In the literature to date, limited studies on external conflicts (EX) have reported diverse results, and also the impacts of government stability (GS) on EF are less explored. This study explores the roles of external conflicts, economic growth, and government stability on EF in the context of SDG-13. The study also contributes to the literature by examining the environmental effects of government stability and external conflicts for the first time in Pakistan. This research uses time-series methodologies on data from Pakistan from 1984 to 2018 for exploring the long-run relations and causal dynamics. The results unfolded that external conflicts stimulate and Granger cause EF and therefore expand environmental deterioration. Thus, limiting conflicts is in the favor of Pakistan to achieve SDG-13. Surprisingly, government stability also poses harmful impacts on environmental quality by enhancing the EF, indicating that stable governments focus on improving economic conditions rather than environmental quality. Moreover, the study proves the validity of the environmental Kuznets curve. Policy suggestions are made to move forward in achieving SDG-13 and to evaluate the effectiveness of government environmental policies.

Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China

The "14th Five-Year Plan" period is a crucial phase for China to achieve the goal of carbon peaking and carbon neutrality (referred to as the "double carbon"). Thus, it is very important to analyze the main factors affecting carbon emissions and accurately predict the change of carbon emissions to achieve the goal of double carbon. For the slow data updates and the low accuracy of traditional prediction models about the carbon emissions, the key factors of carbon emissions change selected by gray correlation method and the consumption of coal, oil, and natural gas were input into four single prediction models: gray prediction model GM(1,1), ridge regression, BP neural network, and WOA-BP neural network to obtain the fitted and predicted values of carbon emissions, which serve as input to the particle swarm optimization-extreme learning machine (PSO-ELM) model together. Based on the PSO-ELM combined prediction method above and the scenario prediction indicators constructed according to relevant policy documents of Chongqing Municipality, the carbon emission values of Chongqing Municipality during the 14th Five-Year Plan period are predicted in this paper. The empirical results show that carbon emissions of Chongqing Municipality still maintain an upward trend, but the growth rate slow down compared with 1998 to 2018. In general, the carbon emission and GDP of Chongqing Municipality showed a weak decoupling state during 1998 to 2025. By calculation, the PSO-ELM combined prediction model is superior to the above four single prediction models in carbon emission prediction and has good property by the robust testing. The research results can enrich the combined prediction method about the carbon emissions and provide policy suggestions for Chongqing's low-carbon development during the 14th Five-Year Plan period.

Environmental impact of globalization: The case of central and Eastern European emerging economies

Against the backdrop of piling environmental concerns in the modern era of globalization, this study aims to check the validity of the Pollution Haven Hypothesis (PHH) in Eastern European emerging countries and the relevance of globalization. The study targets to reduce the lack of consensus on the globalization-economic complexity-environment in European countries. Besides, we also intend to explore the existence of an N-shaped economic complexity-related Environmental Kuznets Curve (EKC) controlling for the bearing of renewable energy on environmental degradation. For analytical purposes, both parametric and non-parametric quantile regression approaches are employed. Overall, we find a non-linear relationship between economic complexity and carbon emissions, and N-shaped EKC is verified. Globalization and renewable energy consumption boost and inhibit emissions, respectively. More importantly, the results confirm the moderating role of economic complexity in neutralizing the carbon emissions-boosting effect of globalization. On the other hand, the non-parametric findings show that the N-shaped EKC hypothesis does not hold for high emissions quantiles. Furthermore, for all emissions quantiles, it is found that globalization boosts emissions, economic complexity, and globalization jointly curbs emissions and renewable energy curbs emissions. Based on the overall findings, some vital environmental development policies are recommended. The conclusions support shaping policy options promoting economic complexity and renewable energy as key factors in mitigating carbon emissions.

Determinants of environmental quality in India: evidence using the bootstrapped ARDL model with structural breaks

This current study examines the impact of renewable energy consumption, agriculture, and globalization on carbon emissions in India over the period from 1980 to 2018. For long-run estimates, we apply Gregory-Hansen's co-integration test, bootstrap ARDL approaches, fully modified ordinary least squares, and dynamic OLS. The empirical results of long-run estimates indicate that a 1% increase in renewable energy consumption, agriculture, and economic globalization will increase carbon emissions by 0.764%, 1.675%, and 0.517%, respectively. Moreover, this study confirms the detrimental effect of these variables on carbon dioxide emissions. Economic globalization coefficients indicate that the scale effect is valid in India. The 2002 economic crisis slowed down the country's growth rate, which reduced the ecological pollution. Several policy recommendations are derived from the empirical findings.

Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools

Stock market price prediction is considered a critically important issue for designing future investments and consumption plans. Besides, given the fact that the COVID-19 pandemic has adversely impacted stock markets worldwide, especially over the past two years, investment decisions have become more challenging for risky. Hence, we propose a two-phase framework for forecasting prices of oil, coal, and natural gas in India, both for pre-and post-COVID-19 scenarios. Notably, the Autoregressive Integrated Moving Average, Simple Exponential Smoothing, and K- Nearest Neighbor approaches are utilized for analyses using data from January 2020 to May 2022. Besides, the various outcomes from the analytical exercises are matched with root mean squared error and mean absolute and percentage errors. Overall, the empirical outcomes show that the Autoregressive Integrated Moving Average method is appropriate for predicting India's oil, coal, and natural gas prices. Moreover, the predictive precision of oil, coal, and natural gas in the pre-COVID-19 period seems to be better than in that the post-COVID-19 stage. Additionally, prices of these energy resources are forecasted to increase through the year 2025. Finally, in line with the findings, significant policy recommendations are made.

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.

Drivers of green growth in the Kingdom of Saudi Arabia: can financial development promote environmentally sustainable economic growth?

The Kingdom of Saudi Arabia has recently declared its vision of turning carbon neutral by 2060. This declaration has motivated policymakers in this Arab nation to design policies that can green economic activities in Saudi Arabia so that environmentally sustainable growth can be ensured. Against this backdrop, this study models the independent and joint effects of financial development, globalization, and energy efficiency rates on green growth of the Saudi Arabian economy. In this regard, green growth in the Kingdom of Saudi Arabia is proxied by the difference between the nation's annual per capita growth rates of gross domestic product and carbon dioxide emission. Utilizing data from 1972 to 2018 and controlling for structural break-induced problems found in the data, the findings from the regression and causality analyses confirm the green growth-inhibiting impacts of financial development and trade globalization. In contrast, greater financial globalization is evidenced to drive green growth in the Kingdom of Saudi Arabia. Furthermore, more efficient uses of energy resources are found to not only directly boost green growth but also partially neutralize the long-run green growth-dampening impacts associated with the development of the financial sector. In addition, financial development and trade globalization are observed to jointly inhibit green growth attainment both in the short and long run. In line with these important findings, it is recommended that the government of Saudi Arabia conceptualizes new green growth policies so that the nation's annual per capita economic growth rate outpaces its annual per capita growth rate of carbon dioxide emissions.