The use of green finance to slow down global warming in support of sustainable development remains widely discussed. This study examines whether investment structure moderates the impact of green finance on the environment in China, one of the top carbon-emitting nations and the second-largest economy in the world. We primarily used the moments-quantile regression approach with fixed-effect models on panel data from 1992Q1 to 2020Q4. First, the results confirmed that green finance and public and private investments worked synergistically to lower CO2 emissions, especially in Central and Western China. However, there was no proof that green finance and foreign direct investment were complementary in reducing CO2 emissions in China, unlike the Central region. Second, green finance marginally lowered CO2 emissions in all provinces, mainly in Eastern and Western China; this reduction was largely dependent on private investment in the Western region’s most polluting areas and foreign direct investment in Eastern and Western China’s least polluting provinces. Third, the beneficial effect of green finance occurred at varying optimal thresholds and investment-related conditions across Chinese regions at different quantiles. Lastly, we showed that in contrast to the variable impacts of urbanization, oil prices, and economic growth across Chinese regions at different quantiles, renewable energy, and trade openness reduced CO2 emissions. In conclusion, the study makes some policy recommendations for China’s sustainable economic development, an important model from which other countries can tailor their investment strategies and environmentally friendly policies.

Fighting global warming has become a vital requirement for environmental sustainability. Green finance has gained popularity as a promising mechanism for transitioning to a lowcarbon economy. Thus, this paper investigates whether excess green financing increases renewable energy capacities and enhances environmental quality from 1992Q1 to 2020Q4 in China, one of the major CO2 emitters. We primarily used the method of moments-quantile regression with fixed-effect models. First, we found nonlinear U-shaped impacts of green finance on wind power capacities in all Chinese regions, thermal power capacities in the Western and Central areas, and hydropower capacities in Eastern China, respectively. Second, we confirmed an inverted U-shaped impact of green finance on CO2 emissions in the Eastern region but U-shaped effects in the Western and Central regions. The impacts of green finance were asymmetrical due to the heterogeneous distributions of renewable energy sources and environmental quality within and between regions. Green finance mostly improved environmental quality when certain conditions and thresholds were met. Third, green finance had substantial marginal effects on environmental quality in the least polluted provinces (Q.20) in Western China and the most polluted provinces (Q.80) in Eastern China. Finally, there were heterogeneous effects of oil prices, urbanization, foreign direct investments, and trade openness on renewable energy consumption and environmental quality across Chinese provinces. Accordingly, this study provides some policy recommendations for China’s sustainable development, a key example from which the international community can adjust its green policies.

China’s energy stocks are confronted with significant climate-related challenges. This paper aims to measure the daily climate transition risk in China by assessing the intensity of climate policies. The daily climate physical risk encountered by China’s renewable energy stocks is also measured based on the perspective of temperature change. Then, the partial linear function coefficient model is adopted to empirically investigate the non-linear impacts of climate transition risk and climate physical risk on the return and volatility of renewable energy stocks. The nonlinear moderating effect of climate transition risk is also involved. It is found that: (1) Between 2017 and 2022, the climate transition risk in China exhibited a persistent upward trend, while the climate policies during this period particularly emphasized energy conservation, atmospheric improvements, and carbon emissions reduction. Additionally, the climate physical risk level demonstrated a pattern consistent with a normal distribution. (2) There is a U-shaped nonlinear impact of climate physical risk on the return and volatility of renewable energy stocks. High climate physical risk could not only increase the return of renewable energy stocks but also lead to stock market volatility. (3) Climate transition risk exhibits a U-shaped effect on the return of renewable energy stocks, alongside an inverted U-shaped effect on their volatility. Notably, a high level of climate transition risk not only increases the return of renewable energy stocks but also serves to stabilize the renewable energy stock market. Moreover, the heightened risk associated with climate transition enhances the negative impact of oil price volatility on the yield of renewable energy stocks and, concurrently, leads to an increase in volatility.The strength of this moderating effect is directly correlated with the level of climate risk.

This study investigates and compares the effects of the Coronavirus Disease 2019 (COVID-19) pandemic, the Chicago Mercantile Exchange (CME)'s negative price suggestion on prices and trading activities in the crude oil futures market to discuss the cause of negative crude oil futures prices. Through event studies, our results show that the COVID-19 pandemic no longer impacts crude oil futures prices in April after controlled market risk, while the CME’s negative prices suggestion can explain the crude oil futures price changes around and around even after April 8 to some degree. Moreover, our study uncovers anomalies in prices and trading activities by analyzing returns, trading volume, open interest, and illiquidity measures using vector autoregressive (VAR) models. The results imply that CME’s allowing negative prices strengthens the price impact on trading volume and makes illiquidity risk matter. Our results coincide with the following lawsuit evidence of market manipulation.

Climate change has become mankind’s main challenge. Greenhouse gas (GHG) emissions from shipping are not irresponsible for this, representing 3% of the global total; an amount equal to that of Germany’s emissions. The Fourth Greenhouse Gas Study 2020 of the International Maritime Organization (IMO) predicts that the proportion of GHG emissions from shipping will rise further, as global trade continues to recover and grow, along with the economic development of India, China and Africa. China and the European Union have proposed to include shipping in their carbon emissions trading systems (ETS). As a result, the study of the relationship between the carbon finance market and the shipping industry, attempted here for the first time, is particularly important both for policymakers and shipowners. We use wavelet analysis and the spillover index methods to explore the dynamic dependence and information spillovers between the carbon finance market and shipping. We discover a long-term dependence and information linkages between the two markets, with the carbon finance market being the dominant one. Major events, such as the 2009 global financial crisis; Brexit in 2016; the 2018 China-US trade frictions; and COVID-19 are shown to strengthen the dependence of carbon finance and shipping. We find that the dependence is strongest between the EU carbon finance market and dry bulk shipping, while the link is weaker in the case of tanker shipping. Nonetheless, carbon finance and tanker shipping showed a relatively stronger dependence when OPEC refused to cut production in 2014, and when the China-US trade dispute led to the collapse of oil prices after 2018. We show that information spillovers between carbon finance and shipping are bidirectional and asymmetric. The carbon finance market is the principal transmitter of information. Our results and their interpretation provide guidance to governments on whether (and how) to include shipping in emissions trading schemes, supporting at the same time the environmental sustainability decisions of shipping companies.

Using Granger Causal and Cointegration Tests, we analyzed the relationship between the open interests of commercial investors, non-commercial invest institution, retail dealers and the oil price in WTI futures market from 2003 to 2012. Found the short positions of commercial investors are determined by the basis and futures price. And the long positions held by commercial investors are for the need of commercial investors and invest institution to hold short positions. The investment institutions adjust the long positions by the change of the futures price. There are the co-integration relationship between the positions hold by Commercial customers and investment institutions. And the changes of short positions hold by investment institutions are affected by the long positions hold by investment institutions. The long positions holds by the commercial investors are affected by the short positions hold by the commercial investors and investment institutions. These founding can help us to understand the relationship and internal influences mechanism between the open interests and price in futures markets.

This study examines the degree of price-integration between aggregate equity market indices of Hong Kong, the Chinese Shanghai and Shenzhen A and B share markets, and the international Brent crude oil price. The application of Vector Autoregressive methods reveals that the regions markets are generally price-segmented with the prominent exception of Shanghai B market which is price-integrated with the domestic A share markets in both Shanghai and Shenzhen. The evidence would suggest that Chinese markets are more heavily influenced by domestic events in the long term than external influences.