The effects of green growth, environmental-related tax, and eco-innovation towards carbon neutrality target in the US economy.

This study aims to examine the nexus between green growth and carbon neutrality targets in the context of the USA while observing the role of ecological innovation, environmental taxes, and green energy. For this purpose, data were collected from 1970 to 2015 for all the variables of interest. This research utilized the quantile autoregressive distributed lag (QARDL) method due to its various benefits, such as depicting the causality patterns based on different quantiles for different variables like green growth, ecological innovation, environmental taxes, and renewable energy. The findings through the QARDL method showed that the error correction coefficient was significant and negative with the expected negative sign for the different quantiles. The findings showed a significant and negative impact of green growth, square of green growth, ecological innovation, and environmental taxes in determining the carbon dioxide (CO2) emissions for the USA's economy under the long-run estimation. Meanwhile, the outcome for the short-term estimation confirmed that the past and lagged values of CO2 emission were significantly and negatively linked with the current and lagged values of CO2 emission. On the other hand, it was found that green growth and square of green growth, ecological innovation, environmental taxes, and renewable energy played their vital role in reducing haze pollution like PM2.5. Besides, this research also covers the limitations and policy implications.

Unlocking the role of energy poverty and its impacts on financial growth of household: is there any economic concern.

The major purpose of this study is to assess racial disparity and energy poverty index by measuring energy poverty index by using data envelopment analysis and regression equation from South Asia (2001-2018). An energy poverty index is quantifying the size and scope of energy poverty, and DEA is used to investigate the relevance of socioeconomic position to multidimensional energy poverty. In multidimensional energy poverty, location, house ownership position, number of dependents, and the age of the main caregiver have an important positive impact. Our research has shown that Bhutan is the most susceptible nation with an energy poverty index of (0.02), Maldives (0.03), and Bangladesh (0.11), while India (0.86) and Pakistan (0.49) are the least likely to be energy poor as regards energy poverty. Of the total energy production, 78% is based on traditional fuels, followed by 12% based on petroleum products. The Gini index indicates a positive association with the energy poverty index at a 5% significance level. This signifies that these socioeconomic indicators positively contribute to the energy poverty index level. This study developed more synchronized policies to eradicate energy poverty and can provide a way forward for policymakers to develop strategies to implement them suitably in the regional power sector.

How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics.

This paper investigates the effect of different categories of essential COVID-19 data from 2020 to 2021 towards stock price dynamics and options markets. It applied the hypothetical method in which investors develop depression based on the understanding suggested by various green finance divisions. Furthermore, additional elements like panic, sentiment, and social networking sites may impact the attitude, size, and direction of green finance, subsequently impacting the security prices. We created new emotion proxies based on five groups of information, namely COVID-19, marketplace, lockdown, banking sector, and government relief using Google search data. The results show that (1) if the proportional number of traders' conduct exceeds the stock market, the effect of sentimentality indexes on jump volatility is expected to change; (2) the volatility index component jump radically increases with the COVID-19 index, city and market lockdown index, and banking index; and (3) expanding the COVID-19 index gives rise to the stock market index. Moreover, all indexes decreased in jump volatility but only after 5 days. These findings comply with the hypotheses proposed by our model.

Does educated labor force is managing the green economy in BRCS? Fresh evidence from NARDL-PMG approach.

It is observed that an educated labor force can increase the absorption capacity of the economy and improve the effectiveness of green technologies that lead to a reduction in potential CO2 emissions. The study investigates whether an educated labor force contributes to the management of the green economy or not in BRCS economies. Panel ARDL-PMG and NARDL-PMG approaches have been employed for empirical analysis for data ranging from 1995 to 2019. According to the ARDL-PMG results, a highly educated labor force contributes to alleviating CO2 emissions in the long run. In contrast, the findings of NARDL-PMG infer that positive component of a highly educated labor force has a significant negative impact on CO2 emissions, while negative component of a highly educated labor force has a positive impact on CO2 emissions in the long run. The study suggests that BRCS countries' policymakers should promote education and training for the labor force to maintain a reduction in CO2 emissions.