Managing urban agglomeration processes in Russia in the context of agglomerative and socio-economic development.

Studies on urban agglomeration typically focus on the management of urban agglomerations as special types of spatial organization. The problem of finding an adequate management model which accounts for the level of spatial connectivity and socio-economic development of a given territory remains in the background. This article identifies urban agglomeration management models in Russia which account for the agglomerative and socio-economic development of a given territory. A methodology for assessing urban agglomeration processes is presented which, unlike existing methodologies, includes an assessment of agglomeration maturity and an assessment of the socio-economic development of urban agglomerations. This methodology is based on conceptual platforms within spatial development theory; new economic geography; and agglomeration theory and cluster development. The methodology was tested on the data of Chelyabinsk Region agglomerations. The study determined that the Chelyabinsk and Gorniy Ural agglomerations are socio-economically developed and highly agglomerated, while the Magnitogorsk agglomeration shows high socio-economic development and low agglomeration. The conurbations are underdeveloped in both areas. The paper gives several recommendations on methods for choosing urban agglomeration management models. The developed methodology can be used to assess agglomerations across Russia and to choose the most adequate management model for a territory depending on its agglomerative and socio-economic development.

Exploring the asymmetric effect of fiscal policy instruments in encountering environmental degradation: proposing an SDG framework for India.

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.

Nexus between biomass energy, economic growth, and ecological footprints: empirical investigation from belt and road initiative economies.

Several emerging economies, including economies in belt and road initiative (BRI), are experiencing difficulty attaining sustainable development goals. The efficient utilization of biomass energy sources plays an essential role in attaining sustainable development goals, especially among developing economies. This study empirically investigates the ecological footprints, biomass energy demand, and per capita income association for 30 BRI economies from 1995 to 2021. The study incorporates cointegration and panel quantile regression (PQR) to identify the relationship among discussed variables. Empirical outcomes indicate a negative significant biomass energy demand and ecological footprints relationship, especially among the economies with high traits of ecological footprints. Moreover, the empirical findings also confirm the negative significant per capita income and ecological footprints relationship, while the square of per capita income approves a significant positive association with ecological footprints. These estimates confirm the EKC hypothesis among per capita income and ecological footprints. The findings of the current study help to determine the optimum level of modern biomass energy consumption, which helps to attain economic growth without compromising ecological sustainability.

Green bond vs. Islamic bond: Which one is more environmentally friendly?

This research investigates the dynamic dependence and causality relationship of the S&P Kensho Clean Energy (CE) and Cleantech (CT) indices with two green bond indices, including the S&P Green Bond Index (GB) and Green Bond Select (GBS) indices, and four Islamic bond indices, including A-, AA-, AAA-, and BBB-graded Sukuk Indices. In the long- and medium-term, the dependence of CE and CT on Sukuk and green bond indices strengthens under normal or bearish market conditions based on quantile cross-spectral (coherency). Overall, among all alternative financing instruments, AA- and A-rated Sukuk indices and GBS indices have higher coherency with CE and CT. Furthermore, the direction of causality in the frequency domain is most commonly observed from Sukuk and bond indices to CT and CE. Once again, AA-rated, A-rated, and GBS have more significant causality effects on CT and CE for nearly all frequencies compared to other indices. These results present vital implications for both policymakers who want to advocate the development of environmentally friendly projects and investors who want to invest their capital in green economic activities in terms of diversifying and hedging their investments.

Examining the avenues of sustainability in resources and digital blockchains backed currencies: evidence from energy metals and cryptocurrencies.

The sustainability issues have been surmounted in the last decades. The digital disruption caused by blockchains and other digitally backed currencies has raised several serious concerns for policymakers, governmental agencies, environmentalists, and supply chain managers. Alternatively, sustainable resources are environmentally sustainable and naturally available resources which are employable by several regulation authorities to reduce the carbon footprint and attain energy transition mechanisms to support sustainable supply chains in the ecosystem. Using the asymmetric time-varying parameters vector auto-regressions approach, the current study examines the asymmetric spillovers between blockchain-backed currencies and environmentally supported resources. We find clusters between blockchain-based currencies and resource-efficient metals, highlighting similar-class dominance of spillovers. We portrayed several implications of our study for policymakers, supply chain managers, the blockchain industry, sustainable resources mechanisms, and regulatory bodies to emphasize that natural resources play a significant role in attaining sustainable supply chains servicing the benefits to society at large and to other stakeholders.

Exploring the asymmetric effect of fiscal decentralization on economic growth and environmental quality: evidence from India.

In recent years, environmental issues have become controversial, and policymakers are discovering new predictors of carbon emissions. Some economists/researchers have advocated for fiscal decentralization to improve the quality of the environment by offering more financial authority to provincial/local and sub-national governments. Therefore, this work aims to inspect the effect of fiscal decentralization on economic growth and environmental quality in India by taking data from 1996 to 2021. This work applies both ARDL and NARDL econometric models for empirical examination. The findings of this study suggest that expenditure decentralization has asymmetric long-term and short-term consequences on economic growth, and carbon emission in India. The result of the asymmetric ARDL model also indicates that positive and negative shock in expenditure decentralization contrarily affects economic growth and carbon emission. Moreover, the positive and negative shock in revenue decentralization helps in reducing carbon emissions both in the long run and short run in India. These outcomes are useful for policy analysis from the Indian economic policy perspective. The study also laid out potential outcomes that may benefit India's local governments and central government in resolving the issues of economic growth and environmental degradation.

Re-evaluating the impacts of green innovations and renewable energy on carbon neutrality: Does social inclusiveness really matters?

This study explores the impact of green innovation and renewable energy on carbon emissions, considering the mediating role of social inclusivity for a panel of 24 countries in the Organization for Economic Co-operation and Development (OECD) from 1994 to 2019. The empirical strategy is framed in a generalized method of moments dynamic panel, which is novel for assessing the short- and long-term relationships among the variables. By controlling for confounders, we assessed the mechanism by which green innovation and renewable energy contribute to carbon emissions. Furthermore, for consistency with prior empirical research, we extended the analysis using alternative statistical specification by Hausman-Taylor and the feasible generalized least squares, which controls for potential endogeneity issues and cross-panel correlation. Our results are relatively mixed: our analysis suggests that green innovation and economic growth are positive and statistically significant predictors of carbon emissions. However, renewable energy and social inclusiveness were both negative and significant predictors of carbon emissions. These results suggest that renewable energy and social inclusiveness can serve as remedies for promoting environmental quality and reducing carbon emissions in OECD countries. Therefore, we recommend promoting the expansion of renewable energy at a lower cost to unserved and underserved communities and promoting social inclusiveness to achieve a net zero emission target.

Time-varying effects of fuel prices on stock market returns during COVID-19 outbreak.

This article explores the impact of fuel price movements on the stock market return of 2020 during the COVID-19 disruptions. In doing so, a monthly data of seven selected stock market indices representing developed and emerging economies globally was used for analysis. The study used a time-varying parameter VAR model to examine a time-varying causal association between oil prices and stock market returns and a novel quantile-causality approach to capture the fluctuations of these markets under COVID-19's varying market conditions. The study further utilises the entropy transfer approach to capture the Granger-causal relationship in the presence of nonlinearities of the data series. The results indicate a high information flow from fuel prices to the FTSE-100, Pacific, and European stock indicies, but not the other way round. The results show that, for the FTSE-100 and the European region, there is a two-way information flow between equities and natural gas, and vice-versa. However, a one-way information flow was established from the stock market to the Pacific and emerging economies.

Comparing asymmetric price efficiency in regional ESG markets before and during COVID-19.

The ever-emerging environmental, social, and governance (ESG) concerns have received significant attention of policymakers, governments, regulation bodies, and investors. Considering the markets volatilities due to economic and financial uncertainties that can drive the informational price inefficiencies across the markets, this study compares the asymmetric price efficiency of regional ESG markets by using an asymmetric multifractal detrended fluctuation analysis before and during COVID-19 crisis. We then examine whether global factors influence the asymmetric efficiency of regional ESG markets. Our findings reveal that COVID-19 outbreak reduced the efficiency of regional ESG markets, except for Europe, which sustained its efficiency even during the pandemic. Moreover, global factors drive the efficiency of regional ESG markets significantly before and during COVID-19. A major implication of our findings stems from the fact that a contagion reduces the efficiency of the markets while stable economic conditions make those markets informationally efficient.

Risk Connectedness Between Green and Conventional Assets with Portfolio Implications.

The increasing concerns of investors toward green bonds and their appealing nature of diversification has motivated the current research to study the risk connectedness between green and conventional assets spanning from August 2014 to December 2020. We first estimate the dynamic equi-correlations through DECO-GARCH. Next, we assess the dynamic and static risk connectedness in the median, extreme low, and extreme high quantiles arguing that spillovers vary across different time periods particularly during economically intense time periods. Finally, we analyzed the hedge ratio and hedge effectiveness between green bonds and other assets. We find that equi-correlations are intense during economic shocks such as the Shale oil crisis, Brexit, US interest rate hike, and COVID-19 pandemic. The volatility analysis at average, lower, and upper quantiles also validate time-varying attributes of green and conventional assets. Further, network figures of green and conventional assets identify potential diversification opportunities. Meanwhile, the hedge effectiveness indicates that green bonds are effective hedge for precious metals and cryptocurrencies. Our findings draw multiple implications for policymakers, green investors, financial market participants, and regulatory authorities regarding flight-to-safety during crisis times and maintaining a diverse portfolio to escape potential losses.

Modelling systemic risk of energy and non-energy commodity markets during the COVID-19 pandemic.

COVID-19 led restrictions make it imperative to study how pandemic affects the systemic risk profile of global commodities network. Therefore, we investigate the systemic risk profile of global commodities network as represented by energy and nonenergy commodity markets (precious metals, industrial metals, and agriculture) in pre- and post-crisis period. We use neural network quantile regression approach of Keilbar and Wang (Empir Econ 62:1-26, 2021) using daily data for the period 01 January 2018-27 October 2021. The findings suggest that at the onset of COVID-19, the two firm-specific risk measures namely value at risk and conditional value of risk explode pointing to increasing systemic risk in COVID-19 period. The risk spillover network analysis reveals moderate to high lower tail connectedness of commodities within each sector and low tail connectedness of energy commodities with the other sectors for both pre- and post-COVID-19 periods. The Systemic Network Risk Index reveals an abrupt increase in systemic risk at the start of pandemic, followed by gradual stabilization. We rank commodities in terms of systemic fragility index and observe that in post COVID-19 period, gold, silver, copper, and zinc are the most fragile commodities while wheat and sugar are the least fragile commodities. We use Systemic Hazard Index to rank commodities with respect to their risk contribution to global commodities network. During post COVID-19 period, the energy commodities (except natural gas) contribute most to the systemic risk. Our study has important implications for policymakers and the investment industry.

The impact of technological innovation on renewable energy production: accounting for the roles of economic and environmental factors using a method of moments quantile regression.

Advancement in renewables is one of the most effective techniques for sustained long-term development, and nations across the globe are making efforts to change their economic and industrial structures in a bid to boost green growth. With the advent of the Fourth Industrial Revolution (4IR), the availability, access, and use of green technologies including renewable energy have significantly improved. Researches on the factors that influence renewable energy production are available. However, we are unaware of any previous research that examines the role of renewable energy innovation in the promotion of renewable energy production. As a result, this study evaluates the impact of technical innovation on green growth from 1993 to 2018, while accounting for real GDP, producer price index, and CO2 emissions. Due to their pivotal status among the developing countries, our study has focused on the BRICS countries. By using a new panel quantile regression augmented with the method of moments, the empirical findings suggest that the influence of renewable energy innovation on renewable energy production is significantly positive across all quantiles. Moreover, the coefficients are generally bigger at the small quantiles, which suggests that countries with smaller renewable energy production per capita (India and South Africa) have a higher probability to experience a greater impact of renewable energy innovation per capita than countries with bigger renewable energy production per capita (Brazil and Russia).

Comparing the asymmetric efficiency of dirty and clean energy markets pre and during COVID-19.

In the backdrop of the recent COVID-19 pandemic, the study examines the comparative asymmetric efficiency of dirty and clean energy markets pre and during the COVID-19 pandemic. For this purpose, we utilize an asymmetric multifractality detrended fluctuation analysis (A-MF-DFA). The study's findings uncover the presence of asymmetric multifractality in clean and dirty energy markets. In addition, multifractality in the energy markets is sensitive to trends, time horizon and major events. More importantly, the results suggest superior efficiency of clean-energy markets compared to conventional energies. We confirm the time-varying nature of market efficiency in the energy markets, and during the recent COVID-19 outbreak, market inefficiencies in the clean and dirty energy markets soared. In this way, the study holds meaningful insights for policymakers, energy policy practitioners, investors, and financial market participants to choose between clean (dirty) investments based on their asymmetric efficiency (inefficiency).

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.

Hydropower, human capital, urbanization and ecological footprints nexus in China and Brazil: evidence from quantile ARDL.

The relations among ecological footprints, biocapacity per capita, gross domestic product per capita, natural resources, urbanization, human capital, and hydroelectric consumption are analyzed from 1971Q1 to 2017Q4 for Brazil and China. The novel quantile autoregressive distributive lag method was employed to analyze the long-run and short-run dynamics of environmental degradation. The findings revealed that economic growth has a positive role in the environmental degradation of both countries. However, human capital, natural resources, and hydropower have heterogeneous effects across quantile distribution and between the two countries. Based on the quantile dynamics of environmental degradation, the present study mentions policy implications for sustainable development.

Dynamic connectedness and spillovers between Islamic and conventional stock markets: time- and frequency-domain approach in COVID-19 era.

This study investigates the dynamic connectedness and spillovers between Islamic and conventional stock markets to reveal the time- and frequency-domain dynamics of the two asset classes under various market conditions. Using the spillover index of Baruník and Krehlík (2018), supplemented by the time-varying parameter vector autoregressions (TVP-VAR) connectedness model, we employ daily stock market indices for Islamic and conventional (G7) markets from November 23, 2015, to September 8, 2021. The findings explicate that the volatility spillovers across and within Islamic and/or G7 markets are time-varying and frequency-dependent but during market turbulences, the conventional stocks are prone to more volatilities than the Islamic stocks. Our findings additionally divulge contagious spillovers among Islamic and conventional stocks during Brexit and the studied COVID-19 period. Relative to mid-and long-term spillovers, we underscore the supremacy of short-term spillovers between Islamic and G7 markets. In turbulent trading periods, investors should utilise knowledge about market patterns and volatility to hedge their positions against lower stock returns, when spillover is more intense. Regulators should pay close attention to spillovers since they undermine cross-market connections. Intriguing findings and their implications are further discussed.

Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID-19 pandemic: Implications for hedging and investments strategies.

This study has been inspired by the emergence of socially responsible investment practices in mainstream investment activity as it examines the transmission of return patterns between green bonds, carbon prices, and renewable energy stocks, using daily data spanning from 4th January 2015 to 22nd September 2020. In this study, our dataset comprises the price indices of S&P Green Bond, Solactive Global Solar, Solactive Global Wind, S&P Global Clean Energy and Carbon. We employ the TVP-VAR approach to investigate the return spillovers and connectedness, and various portfolio techniques including minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio to test portfolio performance. Additionally, a LASSO dynamic connectedness model is used for robustness purposes. The empirical results from the TVP-VAR indicate that the dynamic total connectedness across the assets is heterogeneous over time and economic event dependent. Moreover, our findings suggest that clean energy dominates all other markets and is seen to be the main net transmitter of shocks in the entire network with Green Bonds and Solactive Global Wind, emerging to be the major recipients of shocks in the system. Based on the hedging effectiveness, we show that bivariate and multivariate portfolios significantly reduce the risk of investing in a single asset except for Green Bonds. Finally, the minimum connectedness portfolio reaches the highest Sharpe ratio implying that information concerning the return transmission process is helpful for portfolio creation. The same pattern has been observed during the COVID-19 pandemic period.

COVID-19 and environmental concerns: A rapid review.

COVID-19 has slowed global economic growth and consequently impacted the environment as well. Parallelly, the environment also influences the transmission of this novel coronavirus through various factors. Every nation deals with varied population density and size; air quality and pollutants; the nature of land and water, which significantly impact the transmission of coronavirus. The WHO (Ziaeepour et al., 2008) [1] has recommended rapid reviews to provide timely evidence to the policymakers to respond to the emergency. The present study follows a rapid review along with a brief bibliometric analysis of 328 research papers, which synthesizes the evidence regarding the environmental concerns of COVID-19. The novel contribution of this rapid review is threefold. One, we take stock of the diverse findings as regards the transmission of the novel coronavirus in different types of environments for providing conclusive directions to the ongoing debate regarding the transmission of the virus. Two, our findings provide topical insights as well as methodological guidance for future researchers in the field. Three, we inform the policymakers on the efficacy of environmental measures for controlling the spread of COVID-19.

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.

Forestation, renewable energy and environmental quality: Empirical evidence from Belt and Road Initiative economies.

The project of Belt and Road Initiative (BRI) was initiated by China to enhance the level of economic cooperation among partner economies. The study empirically evaluates the link between renewable energy consumption, forest area and emissions of CO2 among 33 partner economies of BRI for the period of 1986-2018. Study utilizes cointegration and heterogeneous Granger causality framework to explore the long-run and causal linkage among variables. Empirical evidence suggests that expansion in renewable energy consumption and increase in forestation will help to reduce the emissions of CO2 among the economies of BRI. However, the estimated findings of Granger causality reveal feedback causation between renewable energy consumption and forest area and unidirectional causation running from per capita income to environmental quality. The study signifies the importance of investment in renewable energy projects and forest management areas among the partner economies of BRI.