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.

From an industry perspective, this paper applies the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market for theperiod2003/02-2021/10, we find that the impact of geopolitical risk on stock market volatility is highly heterogeneous, and its significance mostly appears in the upper and lower tails. At the market level, China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the current and lagging effects of China’s and U.S. GPR on industry stock market volatility are heterogeneous. Second, for most industries, China’s and U.S. GPR/IS can exacerbate industry stock market volatility both in bullish and bearish markets. In addition, China’s and U.S.GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.

以权证市场的10只产品为例，研究探讨了我国权证产品的波动特征以及相应的风险测度VaR模型，同时运用更加严谨和稳健的Kupic LR检验以及动态分位数回归（Dynamic quantile regression）检验法，对各类不同波动模型和收益分布假定下的VaR估计精度进行了深入的后验分析（Backtesting）。主要实证结果显示，能够刻画波动的非对称杠杆效应以及假定新生量服从“有偏学生分布”的VaR测度模型能更好地刻画我国权证市场的风险状况，但并没有发现能够普遍适用于所有权证产品的VaR计算模型存在。