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.
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.
The impact of China’s green finance policies on renewable energy, clean energy, and other green companies is a hot topic of concern. This study uses the difference-in-differences (DID) model to examine the incentive effect of the Green Credit Guidelines (GCG) on the technological innovation and financial performance of Chinese listed green enterprises. The heterogeneity analysis is carried out from the level of digital finance, green development, and marketization. This study finds that: (1) Green finance is conducive to stimulating the technological innovation and financial performance of green enterprises. (2) Green enterprises in areas with high digital finance levels have a more significant incentive effect on green finance policies, compared to areas with less-developed digital finance. (3) Green enterprises in areas with high levels of green development are more significantly positively affected by green finance policies, compared to areas with less-developed digital finance. (4) The incentive effect of green credit policies on green enterprises in areas with a high degree of marketization is more significant, compared with regions with a lower level of green development. Finally, some policy implications are proposed to provide a reference for China to improve the green financial system to facilitate the financing of green enterprises.
Egg-white protein as one of native proteins contains abundant nitrogen and can be obtained with abundant and inexpensive. Herein, we reported that a novel non-noble metal electrocatalyst for oxygen reduction reaction (ORR) was fabricated by thermal treatment of the precursor (including egg-white protein as the direct nitrogen source, FeCl3•6H2O as the metal source and pretreated carbon black as the support) under an inert atmosphere. The voltammetric methods were used to evaluate the oxygen reduction reaction catalytic activity, stability and methanol-tolerant performance of the catalyst. The structural property of the catalyst was also investigated by X-ray diffraction. The results show that the catalyst achieved at 800 ºC has good activity, high methanol-tolerant and long-term stability. The metal content in this catalyst plays a critical role in improving the oxygen reduction reaction activity. An important finding is the Fe and its oxides are not the catalytically active sites for the oxygen reduction reaction, and their existence may facilitate the formation of the oxygen reduction reaction-active sites only. Besides, the activated amorphous-carbon is also found.
Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.
Predicting CO2 emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO2 emission trading price fluctuation. This model can granulate raw data into fuzzy-information granular components with minimum (Low), average (R), and maximum (Up) values as changing space-description parameters. Furthermore, it can forecast carbon prices’ changing space with Low, R, and Up as inputs to support a vector regression. This method’s feasibility and effectiveness is examined using empirical experiments on European Union allowances’ spot and futures prices under the European Union’s Emissions Trading Scheme. The proposed FIG-SVM model exhibits fewer errors and superior performance than ARIMA, ARFIMA, and Markov-switching methods. This study provides several important implications for investors and risk managers involved in trading carbon financial products.
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