Asymmetric impact of pandemics-related uncertainty on CO2 emissions: evidence from top-10 polluted countries

A machine learning algorithm to explore the drivers of carbon emissions in Chinese cities

As the world's largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China's carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.

When do climate change legislation and clean energy policies matter for net-zero emissions?

Achieving the global decarbonization goal under global conflicts is becoming more uncertain. Within this context, this article seeks to examine the effects of global environmental management and efforts to achieve this goal. Specifically, it investigates the role of democracy, control of corruption, and civil society participation as mechanisms that moderate the impact of environmental policy and legislation, particularly clean energy policy and climate change legislation (laws and regulations), on carbon emissions in highly polluted countries. The empirical results show that (i) the effects of democracy-clean energy policies and climate change legislation are relatively small in reducing carbon emissions; (ii) the effect of controlling corruption-climate change regulations is strong in reducing emissions, meaning that governments with higher control of corruption are more effective at enacting and executing laws and regulations dealing with environmental challenges which help achieve desirable environmental outcomes; (iii) strong civil society participation helps the execution of clean energy policies and climate change legislation to curb emissions, and (iv) the robustness check also provides strong evidence that higher control of corruption can contribute to the effectiveness of these policies and legislation in reducing carbon emissions. Overall, these findings suggest that the efficiency of well-designed environmental policy and legislation should be supported by a combination of higher civil society participation and greater control of corruption that can efficiently enforce such policies and legislation.

Quantile VAR network evidence for spillover effects and connectivity between China's stock markets, green commodities, and Bitcoin

Numerous economic and financial crises, particularly the present crisis in the healthcare sector, have pushed major shock spillover channels over stock marketplaces. This research studied how the shock spillover system is affected by three significant factors: Bitcoins, unpredictability, and the China stock market between 2014 and 2021. While much earlier empirical research has looked at risk dispersion in different financial markets, this article will zero in on green markets. This investigation seeks to accomplish something that has never been done before: determine whether or not green commodities, Bitcoin, and uncertainty impact the performance of the China stock market. The following are significant results based on a quantile vector autoregressive (VAR) connection. (i) A static spillover system indicates that information was widely shared across markets during intense market circumstances. (ii) The global green economy and clean energy marketplaces are the primary sources of knowledge spillover in adverse market conditions. This research elucidates the asymmetrical influence of green products, Bitcoin, and market volatility in China. This is vital due to the dynamic nature of international and regional connections. Recent studies have shown that shock spillovers are excellent for cryptocurrencies such as Bitcoin (BTC), uncertainty indices, and global carbon indexes, but bad for most eco-friendly products.

Role of geopolitical risk, currency fluctuation, and economic policy on tourist arrivals: temporal analysis of BRICS economies

The tourism industry is vulnerable to a range of economic and political factors, which can have both short-term and long-term impacts on tourist arrivals. The study aims to investigate the temporal dynamics of these factors and their impact on tourist arrivals. The method employed is a panel data regression analysis, using data from BRICS economies over a period of 1980-2020. The dependent variable is the number of tourist arrivals, while the independent variables are geopolitical risk, currency fluctuation, and economic policy. Control variables such as GDP, exchange rate, and distance to major tourist destinations are also included. The results show that geopolitical risk and currency fluctuation have a significant negative impact on tourist arrivals, while economic policy has a positive impact. The study also finds that the impact of geopolitical risk is stronger in the short term, while the impact of economic policy is stronger in the long term. Additionally, the study shows that the effects of these factors on tourist arrivals vary across BRICS countries. The policy implications of this study suggest that BRICS economies need to develop proactive economic policies that promote stability and encourage investment in the tourism industry.

Exploring the dynamic and multifaceted effects of globalization on renewable energy sources through advanced nonparametric modeling techniques

There is a growing global demand for clean energy, driven by concerns over global warming and the need to decrease greenhouse gas emissions. This research uses a nonparametric approach to analyze the correlation between industrialization and the use of clean energy sources in 16 countries between 1995 and 2020. We utilize a technique called the local linear dummy variable estimate to monitor the effects of globalization on the development of sustainable power over time. An unfavorable and economically important link was found between industrialization and the use of sustainable energy supplies from 2003 to 2012, as determined by an examination of nonparametric econometric techniques. Still, it started moving in the opposite direction, becoming significant and positive after 2014. In addition, we discover that globalization has various effects on the various indices of the use of renewable energy sources. The research also reveals that the effects of globalization on RES vary across different regions, with some regions experiencing more significant benefits than others. Overall, the study provides valuable insights into the complex relationship between globalization and RES and highlights the need for further research to inform policy decisions and promote sustainable development.

Can the carbon emissions trading improve the enterprise environmental responsibility?

Carbon emission trading is an important environmental policy tool to promote carbon emission reduction. With the panel data of carbon emissions trading (CET) pilots, this paper used the spatial difference-in-difference model (SDID) to test whether CET can affect the enterprise environmental responsibility and the spatial spillover effect of carbon emissions trading. The study found that CET can significantly improve the environmental responsibility level of enterprises in pilot areas. The environmental responsibility level of enterprises in surrounding areas are also improved driven by CET. In the time dimension, the role of CET in improving the enterprise environmental responsibility is gradually increasing. The impact of CET on enterprise environmental responsibility has significant heterogeneity in the dimension of ownership attributes and environmental regulation. Promoting corporate environmental protection investment is the main path for CET to improve the enterprise environmental responsibility.

Integrated development of green finance and green accounting in policy banks

The fundamental purpose of this study is to conduct an inquiry into the efficacy of China's green credit strategy, and that will be the core focus of the investigation. As part of this study, we investigate whether or not businesses that increase the environmental transparency of their operations to the outside world and green innovation within their operations are rewarded with more favorable bank loan terms as a direct result of receiving green credit. Specifically, we look at whether or not these businesses are awarded green credit. Our hypothesis is put to the test by using the difference-in-differences (DID) model and the data that was collected from a sample of 1086 publicly traded Chinese manufacturers over the years 2012 to 2017. According to the data, businesses that improve the quality of their environmental disclosures do not receive an increase in their access to corporate finance. On the other hand, businesses that introduce new environmentally friendly breakthroughs do receive an increase in their access to corporate finance. Our research demonstrates that the root of the problem is corporate green-washing, a practice that is common in regions with low environmental disclosure standards and makes it more difficult for businesses to obtain new loans. This practice is popular in areas where environmental disclosure standards are lax. This is the most basic explanation for why the phenomena occur in the first place. Our findings contribute to the literature on themes including green credit policy, corporate green innovation, environmental transparency, and green-washing, all of which are useful to corporations, governments, and financial institutions.