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This paper uses weekly data from July 01, 2011 to July 09, 2021 to examine the dynamic nonlinear connectedness between the green bonds, clean energy, and stock price around the COVID-19 outbreak in the global markets. By building a time-varying parameter vector autoregression model (TVP-VAR), the comparison analyses of pre- and during the COVID-19 sample groups verify the existence of nonlinear and dynamic correlation among the three variables. First, prior to the COVID-19 pandemic, the simultaneous impacts of clean energy on stock price increased over time. Second, the results of impulse responses at different horizons indicate that green bonds lead to a short-term increase of clean energy, and it exerts an increasingly positive impacts after the COVID-19 outbreak. The COVID-19 has weakened the negative impacts of green bonds on stock price in the medium term. Finally, through the analysis of impulse responses at different points, we find that stock prices will rise when clean energy is subjected to a positive shock, and this positive effect is stronger during economic recovery period than in the other two periods.

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Since carbon price volatility is critical to the risk management of the CO2 emissions trading market, research has focused on energy prices and macroeconomic drivers which cause changes in carbon prices and make the carbon market more volatile than other markets. However, they have ignored whether the impact of carbon price determinants changes when the carbon price is at different levels. To fill this gap, this paper applies a semiparametric quantile regression model to explore the effects of energy prices and macroeconomic drivers on carbon prices at different quantiles. The model combines the advantages of parameter estimation, nonparametric estimation and quantile regression to describe the nonlinear relationship between carbon price and its fundamentals, which do not need to make any assumptions about the random error. Carbon prices are high–tailed and exhibit higher kurtosis, the traditional models which tend to assume that data are normally distributed can’t perform well. Furthermore, the semiparametric model doesn’t need to assume that the data are normally distributed. Therefore, the semiparametric model can effectively model the data. Some new evidence from China’s emission trading scheme (ETS) pilots shows that energy prices and macroeconomic drivers have different effects on carbon prices at high or low quantiles. First, the negative impact of coal prices on carbon prices was greater at the lower quantile of carbon prices in the Shenzhen ETS pilot. However, the effects of coal prices were positive in the Beijing ETS pilot, which may be attributed to great demand for coal. Second, oil prices had greater negative effects on carbon prices at higher quantiles in Beijing and Hubei ETS pilots. This can be attributed to the fact that businesses use less oil when carbon prices are high. For the Shenzhen ETS pilot, the effects of oil prices were positive. Third, natural gas prices have a stronger effect on carbon prices as quantiles increased in the Beijing and Hubei ETS pilots. Lastly, the effects of macroeconomic drivers on carbon prices at low quantiles were stronger in the Shenzhen ETS pilots and higher at the medium quantiles in Beijing and Hubei ETS pilots. These findings suggest that the impact of determinants on the carbon prices at different levels is not constant. Ignoring this issue will lead to a missed warning about the risks of the carbon market. This study will be of positive significance for China’s emission trading scheme (ETS) pilots, in order to accurately monitor the effects of carbon prices determinants and effectively avoid carbon market risks.

Climate change and environmental issues caused by carbon emissions have attracted the attention of governments around the world. Drawing on the experience of the EU, China is actively developing a national carbon emissions trading market, trying to encourage emission entities to incorporate carbon emissions reduction into production and consumption decisions through carbon pricing. Is this scheme an effective market-incentivized environmental regulatory policy? Since China successively launched ETS pilots in 2013, the effectiveness of reducing carbon emissions has become one of the current focus issues. This study uses the difference-in-differences (DID) method to evaluate the impact of ETS implementation on emissions reduction and employs the Super-SBM model in data envelopment analysis (DEA) to evaluate the emission-reduction efficiency of eight ETS pilots in China. We find that the carbon trading policy has achieved emission-reduction effects in the implementation stage, and the greenness of economic growth has a significant positive impact on regional GDP. The establishment of China’s unified carbon market should be coordinated with regional development. Some supporting measures for regional ecological compensation and the mitigation of regional development are yet to be adopted.

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In the context of green nance, whether listed companies in heavily polluting industries can convert the external pressure of environmental information disclosure into internal motivation is critical to achieving environmental governance goals. This paper selects 946 listed companies of 16 heavily polluting industries in the Shanghai and Shenzhen stock markets as samples to explore whether environmental information disclosure can help companies increase bank credit support and reduce debt nancing costs to transform their external pressures into internal motivation. The empirical results show that there is a signi cant positive correlation between environmental information disclosure and bank credit decisions. From the perspective of nancing scale, heavily polluting companies have the inherent motivation to disclose environmental information actively and proactively to obtain more credit support. There is no signi cant relationship between the corporate debt nancing cost and environmental information disclosure. This paper puts forward some critical policy suggestions for government decision makers, heavily polluting enterprises and nancial institutions.

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