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In order to achieve sustainable development objectives, safeguard the ecosystem, combat global warming and preserve biodiversity for a more sustainable and secure future, the ecological footprint (EF) must be reduced. Therefore, embracing holistic methods, emphasizing renewable energy (RN) and environmental taxes (ET) is crucial. Therefore, the present study aims to capture the effect of ET and RN on EF in Germany.

To achieve this aim, the novel Fourier-based Autoregressive Distributive Lag (ADL) cointegration and the time and frequency-based connections among the variables are investigated in this work throughout the 1994–2021 time span using the wavelet analytic methods, including wavelet power spectrum (WPS) and wavelet coherence (WC) methods, respectively.

The study’s results express that (1) RN, ET and EF are cointegrated in the long run; (2) EF and RN have volatility; (3) RN use in Germany prevents environmental deterioration and (4) ET decreases EF.

The research findings imply that Germany needs rigorous environmental restrictions and enforcement of alternate energy sources for energy use plans and sustainable production objectives.

To the best of our knowledge, the effect of RN and ET on EF in Germany has not been comprehensively explored by using newly developed econometrics techniques and a single dataset. Therefore, the study provides important policy implementations for the German government and is also likely to open debate on the concept.

This study aims to investigate the interrelationships and elasticities between the production of renewable energy (RE) and three key variables: oil prices, gross domestic product (GDP) and carbon dioxide (CO₂) emissions.

The research uses panel data and time-series analyses for 10 developed and 16 emerging countries for the period 1976–2018, to identify panel and country-specific elasticity of RE production and dynamic causal relationships between these variables. The study uses an autoregressive distributed lag model to determine the long- and short-run dynamics between RE production and the three variables in each country.

Results show a long-run elasticity between RE and GDP, and short-run dynamics between RE and oil prices and CO₂ emissions in the developed countries. Whereas in the emerging countries category, there were long-run relationships between RE and GDP, CO₂ emissions and oil prices.

Results of this study are in fact crucial and can be applied in the drafting of resilience policies to tackle energy vulnerability as well as sustainable growth. The study results will inform and guide governments on the right policies to stimulate RE production in their own countries in the interests of both their national security and sustainable development globally.

This paper attempts to contribute to the literature in at least two ways. First, research on identifying common determining factors, including socioeconomic factors, in both emerging and advanced economies is considerably scarce. Most of the previous research in this field has focused only on the absolute value of RE production in a particular geographical area. Second, many studies have focused on RE consumption. This research differs from them by focusing on the production of RE. Thus, the main contribution of this study is to fill these gaps. The study also presents novel empirical evidence to determine RE production elasticity from 26 countries.

While science has researched the impact of air pollution on human health, the economic dimension of it has been less researched so far. Renewable energy consumption is an important factor in determining the level of life expectancy and reducing health expenditure. Thus, this study aims to investigate the impact of renewable energy, carbon emissions, health expenditure and urbanization on life expectancy in G-7 countries over the period of 2000–2019.

This study has adopted a novel Method of Moments Quantile Regression (MMQR). Furthermore, as a robustness check for MMQR, the fully modified ordinary least square, dynamic ordinary least squares and fixed effect ordinary least square estimators have been used.

The results indicated that renewable energy consumption, health expenditure and urbanization lead to an increase in life expectancy across all quantiles (5th to 95th), whereas higher carbon dioxide emissions reduce life expectancy at birth across all the quantiles (5th to 95th).

The empirical findings conclude that governments should recognize their potential in renewable energy sources and devise policies such as tax-related regulations, or relevant incentives to encourage further investments in this field.

This paper in comparison to the other research studies used MMQR to investigate the impact of factors affecting life expectancy. Also, to the best of the authors’ knowledge, so far no study has investigated the impact of renewable energy on life expectancy in G-7 countries.

The purpose of this study is to systematically review the state-of-art literature on the net-zero economy in the field of supply chain management.

A systematic literature review of 79 articles published from 2009 to 2021 has been conducted to minimise the researchers' bias and maximise the reliability and replicability of the study.

The thematic analysis reveals that studies in the field of net-zero economy have mostly been done on decarbonisation in the supply chain, emission control and life cycle analysis and environmental and energy management. The findings highlight the strong positive association between digitalisation, circular economy and resources optimization practices with net-zero economy goals. The study also addresses the challenges linked with the net-zero economy at the firm and country levels.

Practitioners in companies and academics might find this review valuable as this study reviews, classifies and analyses the studies, outlines the evolution of literature and offers directions for future studies using the theory, methodology and context (TMC) framework.

This is the first study that uses a structured approach to analyse studies done in the net-zero field by assessing publications from 2009 to 2021.

This paper aims to explore the quantile-specific short- and long-term effects of economic policy uncertainty (EPU) on the efficiency of the green bond market.

This study examines the long-term cointegration relationship and the short-term fluctuation relationship of EPU, WTI crude oil price (WTI) and European Union Allowances price (EUA) with the green bond market efficiency (GBE) using the quantile autoregressive distributed lag method. Additionally, the authors analyze the differences before and after the Covid-19 pandemic.

EPU has a significant positive impact on the GBE before the outbreak. However, during the crisis period, the impact of EPU and WTI was greatly weakened, whereas the impact of EUA was strengthened.

This paper demonstrates the dynamics of GBE and its influencing factors under different periods. The findings provide insights for market participants and policymakers to gain a clearer understanding of the green bond market.

This paper extends the study of green bonds by quantifying the GBE and elucidating the nonlinear relationship between efficiency and independent variables at different quantiles over different periods.

This paper empirically assesses energy efficiency (EE) adoption among firms by examining the factors that drive investment in EE in the Onitsha plastic cluster, South-East, Nigeria.

Self-administered questionnaires were delivered to the selected enterprises. A total of 450 questionnaires were administered of which 423 were certified valid and used for the analysis. A Heckit model was developed and estimated.

Gender, firm size, Joneses effect and expected cost reduction benefits are the significant determinants of EE investment. However, firm structure, government incentives, regulatory requirements and reduction of carbon emission are insignificant drivers of EE investment decisions in the Onitsha plastic cluster.

This paper presents a foremost attempt at analysing the determinants of energy investment in a cluster in Nigeria.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

Energy poverty is a global phenomenon, but its prevalence is enormous in most African countries, with a potential impact on quality of life. This study aims to investigate the impact of energy poverty on the shadow economy.

The study uses panel data from 45 countries in Africa over a period of 1996–2018. Using panel cointegrating regression and panel vector auto-regression model in the generalized method of moments technique.

This study provides that energy poverty deepens the size of the shadow economy in Africa. It also documents that there is a bidirectional causality between shadow economy and energy poverty. Therefore, the two variables can predict each other.

The study suggests that lack of access to clean and modern energy services contributes to the depth of the shadow economy in Africa. African authorities are advised to strengthen rural and urban electrification initiatives by providing adequate energy infrastructure so as to reduce the level of energy poverty in the region. To ensure energy sustainability delivery, the study proposes that the creation of national and local capacities would be the most effective manner to guarantee energy accessibility and affordability. Also, priorities should be given to the local capital mobilization and energy subsidies for the energy poor. Energy literacy may also contribute to the sustainability and the usage of modern energy sources in Africa.

Previous studies reveal that income inequality contributes to the large size of shadow economy in developing economies. However, none of these studies analyzed the role of energy poverty and its implications for underground economic operations. Inadequate access to modern energy sources is likely to deepen the prevalence of informality in developing nations. Based on this, this study provides fresh evidence on the implications of energy deprivation on the shadow economy in Africa using a heterogeneous panel econometric framework. The study contributes to the literature by advocating that the provision of affordable modern energy sources for rural and urban settlements, and the creation of good energy infrastructure for the firms in the formal economy would not only improve the quality of life but also important to discourage underground economic operations in developing economies.

While there are enormous studies on the determinants of environmental degradation, empirical studies on the effect of renewable energy consumption and economic growth on the environment remain limited. The purpose of this paper is to examine the asymmetric effect of renewable energy consumption and economic growth on environmental degradation in 31 selected sub-Saharan African countries spanning from 1990 to 2018.

To examine possible asymmetric effects of the exogenous variables on environmental degradation, we used the panel nonlinear autoregressive distributed lag approach and secondary data was sourced from the World Bank (2021).

The cointegration test results suggest that there is a long-run cointegration among the variables whereas our main findings indicate that environmental degradation responds asymmetrically to changes in renewable energy consumption and economic growth. The results further reveal that both positive and negative shocks in renewable energy consumption reduce environmental degradation. On the other hand, positive and negative shocks in economic growth increase environmental degradation in the long run.

The implications of this study include the need for policymakers in sub-Saharan Africa to encourage the utilization of renewable energy as it reduces environmental degradation. Also, governments in the subregion should gradually replace the usage of fossil fuels by adapting renewable energy sources so as to achieve higher economic growth.

The positive and negative shocks of renewable energy consumption and economic growth on environmental degradation are examined to ascertain their asymmetric relationships.