Transiting toward sustainable development: stochastic convergence of energy diversification using a newly constructed index.

An optimal energy mix is a sine qua non for sustainable development. However, the global energy mix is sub-optimally dominated by fossils which endangers energy security and threatens the attainment of sustainable development. Understanding the convergence of energy series can assist the transition path to optimal energy mix and sustainable development. Thus, research on the convergence of several energy series has gained prominence in recent years. This study extends this important niche in the literature on the convergence of natural resources and environmental series by examining the convergence in energy diversification along several dimensions for a panel of 79 lower-middle, higher-middle, and high-income countries. As a departure from the existing studies, the study employs a novel methodology that allows abrupt or smooth changes through the Fourier approximation of smooth breaks, while including factor structures to test for the presence of unit roots in the relative energy diversification series. The results provide evidence of convergence of the energy diversification series in the majority of the considered countries, with 90% of the sample demonstrating convergence. A disaggregated country analysis was conducted and the findings show that 93% of the lower-middle-income countries are converging, while 95% of the upper-middle-income countries and 87% of the high-income countries are converging. Policy implications of the findings are also discussed.

Impact of Climate Policy Uncertainty (CPU) and global Energy Uncertainty (EU) news on U.S. sectors: The moderating role of CPU on the EU and U.S. sectoral stock nexus.

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.

Renewable energy, forest cover, export diversification, and ecological footprint: a machine learning application in moderating eco-innovations on agriculture in the BRICS-T economies.

The United Nations Climate Change Conference (COP26) recommended that the member nations enhance their technological progression and structural transformation to mitigate the problems of climate change. The BRICS-T countries consisting of Brazil, Russia, India, China, South Africa, and Turkey agreed to implement COP26's policy suggestions. These countries accounted for 40% of global greenhouse gas emissions in 2017, thus posing severe threats to the global environment. The current study explores the role of renewable energy, forest depletion, eco-innovations, and export diversification in impacting the ecological footprint for those BRICS-T countries. We further examine the moderating effect of eco-innovations on agriculture on the BRICS-T nations. The study contributes to the existing literature by providing newer empirical insights on how eco-innovations and export diversification, along with renewable energy, forest cover, and agriculture, affecting the ecological footprint in the BRICS-T nations. It utilizes novel empirical methods like parametric and non-parametric techniques to derive the short-run and long-run empirical results. The empirical findings based on the augmented mean group and the kernel regularized least square methods document that economic growth, agriculture value added, and forest depletion increase the ecological footprint. In contrast, renewable energy and eco-innovations decrease the level of ecological footprint. In the long run, a 1% rise in GDP leads to a rise in the ecological footprint by 0.64% using the augmented mean group (AMG) estimation. The mean marginal effects are - 0.27%, 0.29%, and 0.17% for renewable energy; agriculture and forest cover, respectively, using the kernel-based regularized least square methods. The study suggests that policies designed for controlling the ecological footprints focus on the use of energy efficient technologies, particularly in the agricultural sector.

Financial modeling, risk management of energy and environmental instruments and derivatives: past, present, and future.

In this preface, we investigate the past, study the present, and look for the future of financial modeling, risk management of energy and environmental instruments, and derivatives based on articles selected in this special issue (SI). We also summarize the significant findings of those articles and identify the research trends.

Do volatility indices diminish gold's appeal as a safe haven to investors before and during the COVID-19 pandemic?

This study addresses the research question of whether volatility indices of different asset classes reduce gold's appeal as a safe-haven asset before and during the COVID-19 pandemic. We use daily data for seven volatility indices and gold prices and apply the suitable nonlinear autoregressive distributed lag method to analyze the data. Our results indicate that during COVID-19, only the negative Eurocurrency volatility has diminished gold prices in the long term, whereas in the short term, the positive gold, silver, emerging market, and (lagged) financial market volatilities have diminished gold prices. During the pre-COVID-19 normal period, volatilities in the financial, energy, gold, silver, and eurocurrency markets improved gold prices, whereas in the short term, only lagged negative oil volatility diminished gold prices. A robustness test for the 2011-2015 pre-COVID-19 period reveals that this period is to an extent comparable to that of COVID-19. This study reveals no direct effects from emerging markets volatility on gold prices. Notwithstanding, a long memory in gold prices persists and uneven spillover effects exist. Finally, those volatilities predominantly increase gold prices under the normal economic conditions but decrease gold's appeal as a safe haven during crises in the comparable periods. We delineate the implications for investors.

Effects of Price of Gold on Bombay Stock Exchange Sectoral Indices: New Evidence for Portfolio Risk Management.

Using daily data, this paper examines the relationship between the returns of gold and seven sectoral indices in the Bombay Stock Exchange (BSE) for the period from January 2000 to May 2018. Given the importance of gold in India, there are significant issues in a portfolio selection in that country. By addressing the hedged robust portfolio problems, this paper focuses on three vanilla portfolio problems: the maximum return portfolio allocation, the global minimum variance portfolio problem, and the Markowitz portfolio allocation by using various multiple generalized autoregressive conditional heteroskedasticity (GARCH) models. The paper finds that gold returns are significantly independent of the returns of the BSE sectoral indices. Besides, gold returns can help predict the future returns of the Consumer Durables and the Fast-Moving Consumer Goods indices as well as the Oil & Gas equity indices. Finally, the findings also show that gold hedges against the information technology stock index and serves as a robust portfolio diversification tool. With these new results, this paper offers several implications for investors and risk management purposes.

Innovation, militarization, and renewable energy and green growth in OECD countries.

Within a framework that includes economic activity, real interest rate, grants, and subsidies, we aim to explore the role of renewable energy, technological innovation, and particularly the environmentally damaging militarization in driving green growth, which fosters sustainable economic growth by ensuring the values of natural assets, considering OECD countries. Our examination affirms a positive proposition between the development of renewable energy, technological innovation, and green growth in the long run by implementing the cross-sectional dependency panel autoregressive-distributed lags (CS-ARDL) framework in a dynamic heterogeneous panel setting. The findings also suggest that militarization is antagonistic to green growth. Our decomposed analysis is compatible with our premier analysis, indicating a conducive impact of both biomass and non-biomass types of renewable energy on green growth. We also document a negative association between the real interest rate (RIR) and green growth, while income muddles the results. The robustness tests confirm the sensitivity of our main findings to the magnitude of the subsidies and grants provided to renewable energy. The paper concludes with several policy recommendations.

Environmental implications of increased US oil production and liberal growth agenda in post -Paris Agreement era.

Contextualising on the internationally low oil prices era and historically high oil production in USA and refusal to honour the commitments under Paris Agreement (COP: 21), this study investigates the role of education, oil prices and natural resources on energy demand and CO2 emissions in the USA for the period of 1976-2016. In so doing, we employed a bounds testing approach to cointegration accounting for structural breaks in the series. Key findings suggest the presence of a long-run association between underlying variables. The abundance of natural resources and economic growth of the US economy seem to weigh on environmental quality by increasing energy consumption and carbon emissions. Oil prices show a negative association with energy consumption as well as carbon emissions suggesting that a low oil prices regime can lead to an increase in carbon emissions and energy consumption. Interestingly, education seems to play an important role by reducing energy consumption and carbon emissions, resultantly improving the US environmental quality. Our findings have profound environmental implications in terms of efforts to tackle climate change and meeting the Paris agreement (COP: 21) ambitions with reality and USA policy stance.

The relationship between economic growth and carbon emissions in G-7 countries: evidence from time-varying parameters with a long history.

This paper re-investigates the time-varying impacts of economic growth on carbon emissions in the G-7 countries over a long history. In doing so, the historical data spanning the period from the 1800s to 2010 (as constructed) for each country is examined using the time-varying cointegration and bootstrap-rolling window estimation approach. Unlike the previous environmental Kuznets curve (EKC) studies, using this methodology gives us avenue to detect more than one, two, or more turning points for the economic growth-carbon emissions nexus. The empirical findings show that the nexus between economic growth and carbon emission seems over a long history to be M-shaped for Canada and the UK; N-shaped for France; inverted N-shaped for Germany; and inverted M-shaped (W-shaped) for Italy, Japan, and the USA. In addition, the possible validity of EKC hypothesis is examined for both the pre-1973 and post-1973 sub-periods. Based on this investigation, we found that an inverted U-shaped is confirmed only for the pre-1973 period in France, Italy, and the USA. These empirical evidences provide new insights to policy makers to improve environmental quality using economic growth as an economic tool for the long run by observing changes in the environmental impact of this growth from year to year.

The relationship between energy consumption and fiscal decentralization and the importance of urbanization: Evidence from Chinese provinces.

This study examines the provincial impact of fiscal decentralization, in the form of shifting fiscal responsibilities of expenditures and revenues and others from the central government to lower local governments, as well as rapidly rising urbanization, on energy consumption for 31 Chinese provinces subject to relevant important control variables over the period 2006 to 2015. This analysis has implications for economic growth, efficiency and equality in China. To this end, the study employs multiple panel data analyses and panel quantile regressions to analyse this impact. Our findings show that fiscal decentralization has a non-linear relationship with energy consumption, which confirms the concave behaviour of fiscal decentralization in the Chinese provinces and underscores the validity of the environmental Kuznets curve hypothesis in those provinces, given the control variables. Moreover, increasing Chinese urbanization leads to high demand for energy consumption. Provincial governments can support eco-innovation and eco-investment by pursuing green growth strategies such as the expansion of the service sector and the boosting of the renewable energy sector: particularly when taking into account that coal, at one end of the spectrum, generates a 20 times greater carbon impact than renewables at the other.

Impacts of export quality on environmental degradation: does income matter?

International trade in connection with carbon dioxide (CO2) emissions has been well studied, but export quality in this context has not widely been considered yet. Hence, in this study, we fill this gap by exploring the effects of export quality, economic growth, urbanization, trade openness, and total energy use on CO2 emissions in 63 developed and developing countries around the world. To achieve our objectives, we have used the recent techniques of panel quantile estimators as proposed in Powell (2016) and Canay. Econ J 14 (3): 368-386, (2011), along with several other estimation methods. Our overall empirical evidence shows that the existence of the Environmental Kuznets Curve (EKC) hypothesis depends heavily on the estimation method and on the development stage of the economies considered. Emissions are influenced by the same factors as in the EKC specification, as explored in sensitivity analysis. The results from the panel quantile regression model show that economic growth and total energy use are highly CO2 emissions conducive, while urbanization increases environmental degradation at the higher quantiles, as does export quality, depending on the countries' income levels. Consequently, improvements in export product quality should be prioritized through the production of cleaner products mainly in the lower and upper middle-income countries. There should also be a decrease in total energy use in countries of all income levels. Particularly, policy makers should promote a decrease in export products intensive of fossil fuel energy by prioritizing the use of more renewable energy sources.

Macroeconomic factors and frequency domain causality between Gold and Silver returns in India.

This paper examines the relationship between gold and silver returns in India, using monthly data for the period May 1991 to June 2018. To this end, we employ the recently developed frequency domain rolling-window analysis (which is able to show that transitory high frequency shocks are not equal to permanent low frequency shocks over time), as well as the conditional, partial conditional, difference conditional approaches, in addition to the Toda Yamamoto and frequency domain Granger Causalities methods. Further, the relationship is examined in conditional and unconditional frameworks. To condition the relationship, three macroeconomic variables, namely interest rate, BSE stock index and inflation rate are used as the control variables. The results uncover some interesting predictability patterns that vary along the spectrum. Specifically, by applying the rolling-window analysis, we find mixed results of the causality between the gold and silver markets based on the frequencies of different lengths. Our results provide policy inputs, assist investors and hedgers who wish to invest in these markets by constructing strategies and diversify their portfolios based on different frequencies.