Global chessboard: Analyzing how geopolitical risk shapes renewable energy technology investments.

The allocation of budgets for renewable energy (RE) technology is significantly influenced by geopolitical risks (GPRs), reflecting the intricate interplay among global political dynamics, social media narratives, and the strategic investment decisions essential for advancing sustainable energy solutions. Against the backdrop of increasing worldwide initiatives to transition to RE sources, it is crucial to understand how GPR affects funding allocations, informing policy decisions, and fostering international collaboration to pursue sustainable energy solutions. Existing work probes the nonlinear effect of GPR on RE technology budgets (RTB) within the top 10 economies characterized by substantial research and development investments in RE (China, USA, Germany, Japan, France, South Korea, India, the United Kingdom, Australia, and Italy). Past research largely focused on panel data techniques to delve the interconnection between GPR and RE technology, overlooking the distinctive characteristics of individual economies. Contrarily, existing investigation implements the "Quantile-on-Quantile" tool to explore this association on an economy-particular basis, enhancing the precision of our analysis and offering both a comprehensive global perspective and nuanced perceptions for entire countries. The findings manifest a significant reduction in funding for RE technology associated with GPR across various quantile levels in the chosen economies. The disparities in results spotlight the necessity for policymakers to perform thorough assessments and carry out competent strategies to address the variations in GPR and RTB.

Shaping a greener future: The role of geopolitical risk, renewable energy and financial development on environmental sustainability using the LCC hypothesis.

The recent progress report of Sustainable Development Goals (SDG) 2023 highlighted the extreme reactions of environmental degradation. This report also shows that the current efforts for achieving environmental sustainability (SDG 13) are inadequate and a comprehensive policy agenda is needed. However, the present literature has highlighted several determinants of environmental degradation but the influence of geopolitical risk on environmental quality (EQ) is relatively ignored. To fill this research gap and propose a inclusive policy structure for achieving the sustainable development goals. This study is the earliest attempt that delve into the effects o of geopolitical risk (GPR), financial development (FD), and renewable energy consumption (REC) on load capacity factor (LCF) under the framework of load capacity curve (LCC) hypothesis for selected Asian countries during 1990-2020. In this regard, we use several preliminary sensitivity tests to check the features and reliability of the dataset. Similarly, we use panel quantile regression for investigating long-run relationships. The factual results affirm the existence of the LCC hypothesis in selected Asian countries. Our findings also show that geopolitical risk reduces environmental quality whereas financial development and REC increase environmental quality. Drawing from the empirical findings, this study suggests a holistic policy approach for achieving the targets of SDG 13 (climate change).

Differential benefit of coal and natural gas efficiency in Denmark: How clean is the environmental-related innovation?

Inspired by Denmark's ambitious renewable energy initiatives and its commitment to achieving a substantial 70 percent reduction in greenhouse gas (GHG) emissions by 2030 and achieving net-zero emissions by 2050, this study delves deeper into examining the roles of energy source efficiency, renewable energy utilization, and environment-related technologies spanning the years from 1990 to 2021. A comprehensive array of wavelet tools, including wavelet coherence, wavelet-based ordinary least squares (WBOLS), Continuous Wavelet Transform (CWT), Granger causality, and wavelet correlation, was employed to dissect these dynamics. The primary findings underscore the potential for enhancing environmental sustainability through these key indicators. For instance, employing the WBOLS method reveals that a percent increase in renewable energy consumption translates into an approximate reduction of ∼0.02%, ∼0.03%, and ∼0.54% in GHG emissions in the short-, medium-, and long-term, respectively. Similarly, improvements in energy efficiency yield remarkable outcomes. A one percent increase in the efficiency of natural gas utilization leads to GHG emission reductions of ∼0.44%, ∼0.19%, and ∼0.83% in the short-, medium-, and long-term, respectively. Moreover, a 1 percent enhancement in coal energy efficiency results in GHG emission reductions of ∼0.23%, ∼0.19%, and ∼0.91% in the short-, medium-, and long-term, respectively. Furthermore, the study indicates that a surge of 1% in innovation through environment-related technologies corresponds to GHG emission reductions of ∼0.56%, ∼0.10%, and ∼0.02% in the short-, medium-, and long-term, respectively. The results are notably substantiated by the CWT Granger causality approach. Considering the somewhat modest impact of innovation on GHG emissions, especially in the long-term, the study recommends a deliberate emphasis on the design and formulation of environmentally-related innovations that prioritize attributes such as reliability, durability, and adaptability.

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.

Environmental quality and energy transition prospects for G-7 economies: The prominence of environment-related ICT innovations, financial and human development.

Energy transition and reducing greenhouse gas emissions are fundamental to achieving sustainable development and ensuring a bright and healthy future. To contribute to the empirical literature on these objectives, this study explores the long-term influence of environment-related ICT innovations (EICT) on energy transition and greenhouse gas emissions (GHGE) in G-7 economies for the first time, while considering financial development (FD) and human development (HD). Additionally, the study investigates the moderating role of FD with EICT and HD in energy transition and GHGE. Using a Cross-Sectional Augmented Distributed Lag (CS-ARDL) technique to tackle the issues of cross-sectional dependency and slope heterogeneity, the study evaluated data from 1990 to 2020. The results indicate that EICT, FD, and HD have a significant positive effect on long-term energy transition, and mitigate GHGE in G-7 economies. Furthermore, the influence of EICT and HD on energy transition and GHGE is amplified in the presence of financial development, as evidenced by the moderating effect of FD. Based on these facts, the study suggests various policy measures, such as investing in clean technologies and education, to promote the energy transition and environmental quality in G-7 economies to achieve sustainable development goals.

Neutralizing the surging emissions amidst natural resource dependence, eco-innovation, and green energy in G7 countries: Insights for global environmental sustainability.

The unrelenting surge in global warming in the current era suggests the inevitable need for governments across the globe to embark on policy measures that will help flatten the curve of the surging emissions. Consequently, the concept of carbon neutrality has become a vital policy approach for countries to achieve sustainable development. The present study extends the debates on carbon neutrality by examining the extent to which prominent factors such as natural resource dependence, eco-innovation, and green energy (biofuel and renewable energy) facilitate or hinder strides toward achieving carbon neutral environment in G7 economies. The study considers the additional roles of carbon tax, environmental policy stringency, and financial development in longitudinal data ranging from 1997 to 2019. The verification of the stated hypotheses hinges on a battery of estimators comprising cross-sectional ARDL, common correlated effects mean group, augmented mean group, and panel quantile regression. The empirical findings show that green energy, carbon tax, and environmental policy support the drive towards carbon neutrality by reducing the stock of CO2 emissions. On the other hand, natural resource dependence and financial development hinder the carbon neutrality agenda by escalating the surge in CO2 emissions. Robustness analyses are conducted from the angle of an additional outcome variable and estimation technique of which the results corroborate the empirical regularity of the main findings. Policy implications are derived from the empirical findings.

Corrigendum to "CO2 behavior amidst the COVID-19 pandemic in the United Kingdom: The role of renewable and non-renewable energy development" [Renew. Energy 189 (2022) 492-501].

[This corrects the article DOI: 10.1016/j.renene.2022.02.111.].

Asymmetric effect of structural change and renewable energy consumption on carbon emissions: designing an SDG framework for Turkey.

A plethora of studies have shown that structural change helps nations achieve socioeconomic growth. The influence of structural change on environmental quality, on the other hand, has yet to be thoroughly investigated. As a result, the current study assesses the asymmetric impact of structural change on CO2 emissions while controlling for the effects of economic progress, renewable energy utilization, and nonrenewable energy in Turkey. To this end, this research used yearly data stretching from 1965 to 2019. The study applied several econometric approaches including nonlinear auto-regressive distributed lag (NARDL) and spectral causality to assess these associations. The outcomes from the NARDL reveal that variations in the regressors have a nonlinear influence on CO2 in Turkey. Moreover, the transition in the economy's structure helps to boost ecological quality, while the findings also show that Turkey's current economic progress trajectory is unsustainable due to the country's reliance on fossil fuel-based energy consumption. The outcomes of the spectral causality test also show that structural change can predict CO2 emissions in Turkey at different frequencies. Based on the study findings, the government should encourage investment in the service sector in order to maintain a suitable level of environmental sustainability.

CO2 behavior amidst the COVID-19 pandemic in the United Kingdom: The role of renewable and non-renewable energy development.

The spread of the COVID-19 pandemic since the end of 2019 has forced an unprecedented lockdown worldwide, and environmental quality was significantly affected by the pandemic and its induced lockdown. The objective of this study is to examine the role of renewable energy, non-renewable energy and COVID-19 case on CO2 emission in the context of United Kingdom. Several non-linear techniques such as Fourier ADL cointegration test, Non-Linear ARDL, Markov switching regression, and Breitung and Candelon (BC) causality test are employed to attain this objective. The result reveals that there is long run cointegration among the variables in this study. The results demonstrate that positive (negative) shift in renewable energy development decrease (increase) CO2 emissions while positive (negative) shocks in fossil fuel energy increase CO2 emissions. Moreover, negative (positive) variation in COVID case leads to a decrease (increase) in CO2 emissions. Moreover, an uni-directional causal impact was found to run from all the variables - renewable energy, fossil fuel, and COVID-19 case to CO2 emissions. Finally, several policy recommendations are provided.

Role of political risk to achieve carbon neutrality: Evidence from Brazil.

The current research assesses the impact of political risk on carbon dioxide (CO2) emissions in Brazil while controlling the role of financial development, GDP growth, trade openness, and technological innovation. In doing so, the quarterly dataset from 1990 to 2018 is utilized with Bayer and Hanck cointegration, dynamic ordinary least square (DOLS) and canonical correlation regression (CCR), and frequency-domain causality tests. The cointegration test revealed a long-run association amongst the variables of interest. Furthermore, the outcomes from the DOLS and CCR revealed that increasing financial development, technological innovation, trade openness, and real growth increase CO2 emissions while a better political environment reduces environmental pollution.

Evaluating the role of financial globalization and oil consumption on ecological quality: A new perspective from quantile-on-quantile granger causality.

Global warming has progressed into a pressing global concern, primarily driven by human activities. To address this issue, it is vital to identify the key drivers of ecological quality and develop effective policies in response. Consequently, this study seeks to empirically examine the causal effect of financial globalization, economic growth, economic policy uncertainty, and oil consumption on the load capacity factor (LF) in Brazil. The analysis utilizes quarterly data spanning from 1990 to 2021. In this pursuit, the study introduces an array of quantile-based methodologies, encompassing quantile ADF, PP, and KPSS tests, as well as the innovative Quantile-on-Quantile Granger Causality (QQGC) approach. The QQGC represents a notable advancement beyond traditional quantile Granger causality (QGC) methods, as it accounts for the conditional distribution of dependent and independent variables. This study bridges a critical gap in the existing literature by introducing the QQGC to capture the causal influence of the regressors on LF. The findings derived from the QQGC analysis indicate that financial globalization, economic growth, economic policy uncertainty, and oil consumption significantly predict LF across all quantiles. These results offer valuable insights that can inform the formulation of effective policies and strategies aimed at addressing ecological quality and mitigating the impacts of global warming.

Assessing the environmental sustainability corridor: linking oil consumption, hydro energy consumption, and ecological footprint in Turkey.

Climate change has been a topic of significant discourse and debate among scholars and policy makers for several decades. In recent decades, it has become a major problem for the entire human race. Therefore, the present research evaluates the impact of oil consumption, hydro energy use, population density, and economic growth on ecological footprint in Turkey for the period from 1965QI to 2018Q4. This paper uses the BDS test to assess the nonlinearity of the variables in the pre-estimation analysis. The results of the test reveal that non-linearity occurs in all of the variables used in this study. As a consequence, using traditional linear methodologies would produce erroneous results. Our research uses the quantile techniques (quantile cointegration, quantile causality, quantile-on-quantile regression), which are recently developed nonlinear estimate methodologies to assess these associations. The results from the study reveal that oil consumption, hydro energy use, population density, and economic growth contribute to environmental degradation in Turkey in majority of the quantiles. The Granger Causality in Quantiles result also gives credence to the results. The study proposes policy recommendation based on these results.

Do natural resource volatilities and renewable energy contribute to the environment and economic performance? Empirical evidence from E7 economies.

The economies of the emerging seven (E7) are not insulated from the climate change challenges, which is a key concern for most countries. The E7 nations have undertaken part in initiatives to combat climate change, particularly in terms of reducing CO2 emissions from the trajectory of productivity expansion in their countries. It is for this reason that this study examines the impact of resource volatility, renewable energy, and fossil fuel on both economic performance and CO2 emission from 1990 to 2018. The present study used panel quantile regression and Driscoll-Kraay fixed effect-OLS estimators to examine these associations. From model I, the outcome shows that economic performance, natural gas rent, coal rent, and fossil fuel impact CO2 emission positively. Moreover, oil rent, renewable energy, investment in energy, and the interaction between investment in energy and renewable energy also negatively and significantly impact CO2 emission. On the other hand, model II which has economic performance as a dependent variable shows that all the understudy variables have significant positive relations with economic performance. Based on the empirical outcome, policy ramifications are provided.

Formulating sustainable development policies for China within the framework of socioeconomic conditions and government stability.

Improvement in the environmental quality has been seen as a major concern worldwide, and a crucial agenda of the Sustainable Development Goals (SDG's). In this study, we examined the causal impacts of economic growth, financial development, nuclear energy, government stability, and socioeconomic conditions on the environmental quality of China, using quarterly data, covering the period 1984-2018. We used different proxies for environmental quality, i.e., Carbon dioxide (CO2) emissions, ecological footprints, and load capacity factor, and a unique methodology named Fourier quantile causality, recently recommended by Cheng et al. (2021), for the first time in case of China. We find that nuclear energy, and government stability are positively causing the environmental quality, while economic growth, financial development, and socioeconomic conditions are degrading the environmental quality in China, since they are linked with increased income and energy consumption. Furthermore, all the explanatory variables are largely sensitive on different quantiles to affect the environmental quality; however, economic growth is a highly causing environmental degradation. Our study suggests that increasing nuclear energy consumption along with green financial measures that simultaneously support economic growth and socioeconomic conditions can be effective tools for improving environmental quality and realizing the SDGs in China.

Analysing the waste management, industrial and agriculture greenhouse gas emissions of biomass, fossil fuel, and metallic ores utilization in Iceland.

With Iceland's CAP 2020, the country aims significant improvement in the state of its environment through reduction in greenhouse gas (GHG) emission especially in energy production and small industry, waste management, ships and ports, land transport, and agriculture by 2030. Considering this ambition, this study queries whether the consumptions of domestic materials i.e., DMC (especially metallic ores, biomass, and fossil fuels) exhibit differential impact on (i) aggregated greenhouse gas emissions i.e., GHG, (ii) waste management greenhouse gas emission i.e., WGHG, (ii) industrial greenhouse gas emission i.e., IGHG, and (iv) agriculture greenhouse gas emission i.e., AGHG during the period 1990 to 2019. By using Fourier function approaches, the investigation establishes that metallic ores DMC spur GHG, but biomass and fossil fuel DMC mitigate GHG in the long run. Additionally, biomass DMC mitigates AGHG and WGHG by respective elasticities of 0.04 and 0.025 in the long run. While IGHG is significantly reduced by fossil fuel DMC with elasticity of 0.18 in the long run, the AGHG and WGHG are unaffected by the consumption of fossil fuel domestic materials. Moreover, metallic ores DMC spurs only IGHG by elasticity of ∼0.24. The overall evidence shows the need for more stringent material use and resource circularity (especially for metallic ores and fossil fuels) for the country to stay on course of the CAP 2020 and maintain environmental sustainability.

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.

World pandemic uncertainty and German stock market: evidence from Markov regime-switching and Fourier based approaches.

This study aims to examine the impact of the world pandemic uncertainty index on the German stock market index (DAX index) for the 1996Q1 to 2020Q3 period while controlling real effective exchange rate, industrial production index, and consumer price index. The present study performs the Fourier Augmented Dickey-Fulle Unit Root, Fourier Engle-Granger Cointegration, Bayer-Hanck Cointegration, and Markov switching regression tests. The outcomes disclose that there is a long-run cointegration association between the stock market index and world pandemic uncertainty index, real effective exchange rate, industrial production index, and consumer price index in Germany, indicating that the combination of these factors significantly affects the German stock market index in the long-run. Moreover, in both high and low volatile regimes, the world pandemic uncertainty index and real effective exchange rate negatively affect the German stock market index while industrial production and consumer price indices impact positively.

Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm.

The study aims to investigate the financial technology (FinTech) factors influencing Chinese banking performance. Financial expectations and global realities may be changed by FinTech's multidimensional scope, which is lacking in the traditional financial sector. The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization. The future of FinTech will be shaped by technologies like the Internet of Things, blockchain, and artificial intelligence. The involvement of these platforms in financial services is a major concern for global business growth. FinTech is becoming more popular with customers because of such benefits. FinTech has driven a fundamental change within the financial services industry, placing the client at the center of everything. Protection has become a primary focus since data are a component of FinTech transactions. The task of consolidating research reports for consensus is very manual, as there is no standardized format. Although existing research has proposed certain methods, they have certain drawbacks in FinTech payment systems (including cryptocurrencies), credit markets (including peer-to-peer lending), and insurance systems. This paper implements blockchain-based financial technology for the banking sector to overcome these transition issues. In this study, we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors' algorithm. The chaotic improved foraging optimization algorithm is used to optimize the proposed method. The rolling window autoregressive lag modeling approach analyzes FinTech growth. The proposed algorithm is compared with existing approaches to demonstrate its efficiency. The findings showed that it achieved 91% accuracy, 90% privacy, 96% robustness, and 25% cyber-risk performance. Compared with traditional approaches, the recommended strategy will be more convenient, safe, and effective in the transition period.

A time-varying approach to the nexus between environmental related technologies, renewable energy consumption and environmental sustainability in South Africa.

Ecological degradation is a major challenge for all nations. The problem is particularly worrying for South Africa, which has recently suffered from various ecological catastrophes. Thus, the empirical study evaluates the nexus between CO2 emissions and financial development, renewable energy, economic growth and environmental-related technologies in South Africa utilizing data between 1980 and 2020. We employed autoregressive distributed lag (ARDL) and time-varying causality to evaluate these connections. The results from the ARDL show that financial development and environmental-related technologies lessen CO2 emissions while economic progress intensifies CO2 emissions. Surprisingly, renewable energy does not mitigate CO2 emissions. Furthermore, the time-varying causality shows that all the independent variables can forecast CO2 emissions at different sub-periods. Finally, our results are resilient to various policy ramifications useful in reducing CO2 emissions and associated adverse ecological consequences.