What do we learn from Nexus between trade diversification and structural change: informing the future about climate action and Sustainability.

Economic complexity is considered key a driver of social change, structural change, and economic development. Economic complexity is mostly used to capture issues apropos product diversification of exports, trade, technological innovation, human knowledge, and skills. The current study has conducted a detailed bibliometric review of economic complexity, export quality, and trade diversification. In doing so, the authors used the literature up to 2021 to unveil economic complexity's contextual information that witnessed structural change, social change, and trade indicators. The current study is the first integrative review to report the theoretical contribution, future research agendas, and thematic analysis of economic complexity, export quality, and export diversification. Our study, on the subject of economic complexity, export diversification, and import diversification in the period from 1966 to 2021, was carried out by systematically scanning 386 documents, and it is one of the pioneering studies in this field. In addition, economic diversity, development, and economic complexity; export diversification, import diversification, trade openness, and economic growth; energy, environmental Kuznets curve, and economic complexity; and sustainability and economic diversification are the four main research topics of the study. The findings are discussed apropos of economic complexity and exports, methodological aspects of economic complexity, and environmental issues nexus with economic complexity. The current study reports novel findings toward a path for achieving SDG-9 (industry and innovation) and SDG-13 (climate action). The biometric review enables researchers and policymakers to understand export quality, economic complexity, and the trade nexus and report future research directions for achieving sustainable growth in industries and innovation.

Environment-growth nexus and corruption in the MENA region: Novel evidence based on method of moments quantile estimations.

This study contributes to the environment-growth nexus literature by examining corruption's effect on environmental quality in 17 Middle East and North Africa (MENA) countries between 1984 and 2018. We use four different indicators of environmental degradation (ecological footprint, carbon dioxide emissions, greenhouse gas emissions, and carbon emission intensity) to gain a comprehensive view of the relationship. This study's methodological value added is the application of the method of moments quantile regression, which enables us to account for different relationships between the independent variables and environmental quality at different levels of environmental degradation. The results indicate that corruption worsens environmental quality based on three of the four indicators of environmental degradation, while the impact appears to become less severe at higher levels of environmental deterioration. The results also indicate that traditional mean-based panel data estimation methods provide an incomplete picture of the factors behind environmental problems: The explanatory variables' impacts tend to vary at different levels of environmental quality, and the impact's sign can even change when moving from the lowest to the highest environmental degradation quantiles. Overall, the results highlight the importance of curbing corruption to enable enforcement of more stringent environmental regulations.

The influence of shadow economy, environmental policies and geopolitical risk on renewable energy: A comparison of high- and middle-income countries.

Given the alarming rate of climate change and environmental degradation, major countries are seeking ways to curtail environmental damage and attain sustainability in the future. In the quest for a green economy, countries are motivated to adopt renewable energy that can assist in resource conservation and efficiency. Accordingly, this study examines the diverse effects of the underground economy, environmental policy strictness, geopolitical risk, gross domestic product, carbon emissions, population, and oil prices on renewable energy for 30 high- and middle-income countries from 1990 to 2018. The empirical outcomes based on quantile regression document significant variations across two country groups. For instance, for high-income countries, the shadow economy has a detrimental effect across all quantiles but it is statistically significant at the top quantiles. Nonetheless, the effect of the shadow economy on renewable energy is detrimental and significant statistically across all quantiles for middle-income countries. In the context of environmental policy stringency, the effect is positive across both country groups, though there is heterogeneity in outcomes. Geopolitical risk has a positive influence on the deployment of renewable energy for high-income countries but negatively impacts renewables for middle-income countries. As far as policy suggestions are concerned, the policymakers of both high- and middle-income countries need to take steps to constrain the growth of the shadow economy by adopting effective policy strategies. Policies need to be implemented for middle income-countries to reduce the unfavorable effect of geopolitical uncertainty. The findings of this study contribute to a better and more precise understanding of factors shaping the role of renewables whereby the energy crisis would be mitigated.

Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis.

We provide the first empirical study on the role of panic and stress related to the COVID-19 pandemic, including six uncertainties and the four most traded cryptocurrencies, on three green bond market volatilities. Based on daily data covering the period from January 1, 2020 to January 31, 2022, we combine Diebold and Yilmaz's (2012, 2014) time domain spillover approach and Ando et al.'s (2022) quantile regression framework to investigate the time-frequency spillover connectedness among markets and measure the direction and intensity of the net transmission effect under extreme negative and positive event conditions, and normal states. We further provide novel insights into the green finance literature by examining sensitivity to quantile analysis of the net transfer mechanism between green bonds, cryptocurrencies, and pandemic uncertainty. Regarding the network connectedness analysis, the results reveal strong net information spillover transmission among markets under the bearish market. In extremely negative event circumstances, the MSCI Euro green bond acts as the leading net shock receiver in the system, whereas COVID-19 fake news appears as the largest net shock contributor, followed by BTC. According to sensitivity to quantile analysis, the net dynamic shock transfer mechanism is time-varying and quantile-dependent. Overall, our work uncovers crucial implications for investors and policymakers.

Impact of economic policy uncertainty, geopolitical risk, and economic complexity on carbon emissions and ecological footprint: an investigation of the E7 countries.

There is a plethora of studies on the energy-consumption-environmental-quality nexus. Nevertheless, empirical research on the impact of global uncertainties on environmental quality is lacking. This study contributes to the literature by examining the impact of economic policy uncertainty (EPU), geopolitical risk (GPR), and economic complexity on the ecological footprint and carbon emissions of E7 economies for the period 1995-2018. Our empirical results indicate a long-term relationship between economic complexity, EPU, GPR, energy consumption, and two environmental quality indicators, carbon dioxide emissions and ecological footprint. In the long run, a divergence from disequilibrium takes 3 years to return to the equilibrating position. The environmental effects of key determinants are different in terms of direction, magnitude, and time span. Specifically, an inverted U-shape describes the relationship between economic complexity and environmental degradation in the long-term only, which confirms the environmental Kuznets curve (EKC) hypothesis. The environmental effects of EPU and GPR are harmful in the short run but prove to be beneficial in the long run. Higher energy consumption significantly degrades environment quality as expected. Based on these findings, the paper provides several useful suggestions for policymakers in the context of E7 countries.

Analyzing the Nexus Between Geopolitical Risk, Policy Uncertainty, and Tourist Arrivals: Evidence From the United States.

This study attempts to explore the causal linkage of the COVID-19 pandemic, economic policy uncertainty, geopolitical risk, and tourism arrivals in the United States taking data from January to November 2020. In order to analyze the above relationship, this study uses a novel time-varying granger causality test developed by Shi et al. (2018), which incorporates its three causality algorithms such as forward recursive causality, rolling causality, and recursive evolving causality. The findings from forward recursive causality could not confirm any significant causal relationship between COVID-19 and tourism, geopolitical risk (GPR) and tourism, economic policy uncertainty and tourism, and geopolitical risk and COVID-19 but found causality between economic policy uncertainty and COVID-19. The rolling window causality reported bidirectional causality between COVID-19 and tourism and unidirectional causality running from tourism to geopolitical risk. However, the recursive evolving causality identified a significant bidirectional causal relationship between all the variables. Based on the findings, policy implications for the tourism sector are provided.

The spillover effects and connectedness among green commodities, Bitcoins, and US stock markets: Evidence from the quantile VAR network.

Several economic and financial crises, as well as the recent health crisis, have driven major shock spillover channels over stock markets. While various empirical studies have explored risk transmission among financial markets, this study examined the shock spillover network across green markets, including the energy, solar, wind, water, and environment markets, as well as Bitcoins, uncertainty, and the US stock market. Indeed, this paper is the first study to ascertain whether green commodities, Bitcoins, and uncertainty are connected to the US stock market. Using the quantile vector autoregressive (VAR) connectedness framework, the key findings are as follows: (i) A static spillover network showed there was high spillover transfer between markets at extreme market states. (ii) Global green economy and global clean energy markets act as the highest contributors of information spillover under bearish market scenarios. (iii) With regard to dynamic connectedness, this work highlights the asymmetric spillover effect of green commodities, Bitcoins, and uncertainty on the US stock market. (iv) Bitcoin (BTC), uncertainty, and global carbon indexes were found to be net receipts of shock spillovers, while most green commodities acted as net contributors. (v) Significant implications for environmental and financial investors as well as policymakers are provided.

Appraisal of CO2 emission in Tunisia's industrial sector: a dynamic vector autoregression method.

The world is confronted with a slew of environmental issues, one of which is attenuating the detrimental impacts of carbon dioxide (CO2) emission-induced climate change. The ever-increasing use of energy is eroding natural resources to the point that our economic future may be jeopardized. The Tunisian economic growth indicates the excellent performance in the industrial sector as the minimum required input for these developments which necessitate additional energy consumption, resulting in increased CO2 emissions and environmental degradation. This study explores the role of energy efficiency, urbanization, economic growth, and natural gas energy usage in the industrial sector on the CO2 emissions of Tunisia. The research mainly employs the vector autoregressive model (VAR) to examine the factors driving the evolution of CO2 emissions through the industrial sector from 2000 to 2018. The findings assess that natural gas as an energy source and efficiency is crucial for reducing CO2 emissions. The study has shown the existence of the environmental Kuznets curve (EKC), which demonstrates that economic development in Tunisia has an inverted U-shape connection with CO2 emissions. The findings show that energy consumption and GDP have a considerable impact on CO2 emissions due to large-scale population changes and industrial structure alteration. In contrast, energy efficiency is a key factor in lowering CO2 emissions. Based on the study's results, the article will enable economic decision-makers and relevant authorities to develop an appropriate energy strategy for the industrial sector to safeguard environmental deterioration in the long term by lowering carbon emissions.

Investigating the role of export product diversification for renewable, and non-renewable energy consumption in GCC (gulf cooperation council) countries: does the Kuznets hypothesis exist?

This study explores the effects of renewable and nonrenewable energy demand on export product diversification, economic growth, natural resources, human capital, and trade in GCC (Gulf Cooperation Council) countries using data of six countries from 1990 to 2019. The empirical analysis integrates the panel unit root tests (IPS and CIPS), panel quantile regression, and fully modified OLS models. The empirical results confirm that there exists a significant negative relationship between renewable energy and export diversification; signifying that diversification of products will reduce renewable energy. Similarly, when compared to the square of export product diversification, it shows a positive and significant correlation. The empirical findings highlighted the presence of Kuznets's hypothesis between export product diversification, renewable, and non-renewable energy consumption. Furthermore, the findings suggest that natural resources and economic growth may increase overall energy consumption in GCC countries. It implies an important policy suggestion that encouraging export diversification will reduce GCC countries' reliance on oil to meet energy demand.

Investigating the Effects of Meteorological Parameters on COVID-19: Case Study of New Jersey, United States.

This research aims to explore the correlation between meteorological parameters and COVID-19 pandemic in New Jersey, United States. The authors employ extensive correlation analysis including Pearson correlation, Spearman correlation, Kendall's rank correlation and auto regressive distributed lag (ARDL) to check the effects of meteorological parameters on the COVID new cases of New Jersey. In doing so, PM 2.5, air quality index, temperature (°C), humidity (%), health security index, human development index, and population density are considered as crucial meteorological and non-meteorological factors. This research work used the maximum available data of all variables from 1st March to 7th July 2020. Among the weather indicators, temperature (°C) was found to have a negative correlation, while humidity and air quality highlighted a positive correlation with daily new cases of COVID-19 in New Jersey. The empirical findings illustrated that there is a strong positive association of lagged humidity, air quality, PM 2.5, and previous infections with daily new cases. Similarly, the ARDL findings suggest that air quality, humidity and infections have lagged effects with the COVID-19 spread across New Jersey. The empirical conclusions of this research might serve as a key input to mitigate the rapid spread of COVID-19 across the United States.

European commitment to COP21 and the role of energy consumption, FDI, trade and economic complexity in sustaining economic growth.

The nexus between economic growth and energy consumption has been exhaustively explored, yet the empirical evidence and the theoretical points of view remain at odds. This study contextualises and capitalises on this discrepancy and examines the connection between non-renewable and renewable energy consumption and economic growth, considering the moderating impact of economic complexity, trade openness, FDI and institutional quality. We use a panel quantile regression model and data from 32 European countries in the period 1995-2014. Our key results show that economic complexity, renewable energy consumption, trade openness, FDI and institutional quality enhance economic growth. The results for non-renewable energy consumption showed both a positive and a negative impact in different quantiles, indicating that the consumption of renewable energy is in fact more effective for economic growth than the use of non-renewables. Our findings have far-reaching implications for stakeholders and policymakers working on sustainable economic growth and energy policy with a view to meeting the commitments made under the Paris Agreement (COP21).

Does trade matter for environmental degradation in developing countries? New evidence in the context of export product diversification.

Global warming is a serious problem facing the world today. To minimize it, scholars are trying to find the reasons behind increasing CO2 emissions. This study examines the effects of overall export product diversification, extensive margin, and intensive margin on CO2 emissions as indicators of environmental degradation in 84 developing countries for the period of 1971-2014 in the context of the environmental Kuznets curve (EKC) hypothesis using three estimators, namely, autoregressive distributed lag (ARDL) bounds test, dynamic ordinary least squares (DOLS), and fully modified ordinary least squares (FMOLS). The empirical findings reveal that EKC hypothesis is valid. The overall empirical findings from various approaches show that overall diversification, extensive margin, and intensive margin have a positive and significant effect on CO2 emissions.

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.

Does economic complexity matter for environmental degradation? An empirical analysis for different stages of development.

This study is among the first attempts to examine the effect of economic complexity as an indicator of sophisticated and knowledge-based production structures on CO2 emissions for 55 countries over the period of 1971-2014. The countries considered fall into three different income groups, namely high income, higher middle income, and lower middle income. The study employs the panel quantile regression methodology and tests the existence of the environmental Kuznets curve (EKC) hypothesis by including economic complexity and other control variables such as energy consumption, urbanization, and trade openness in its model. The results show that economic complexity has significant impacts on the environment. Based on the analysis, economic complexity has increased the environmental degradation in lower and higher middle-income countries, and has controlled CO2 emissions in high-income countries. Since economic complexity plays a significant role in environmental damage, it is crucial for low- and middle-income countries to adjust their current industrial and production policies to promote economic growth and at the same time protect the environment.