In the face of severe climate change, researchers have looked for assistance from financial instruments. They have examined how to hedge the risks of these instruments created by market fluctuations through various green financial derivatives, including green bonds (i.e., fixed-income financial instruments designed to support an environmental goal). In this study, we designed a green bond index option contract. First, we combined an autoregressive moving-average model (AMRA) with a generalized autoregressive conditional heteroskedasticity model (GARCH) to predict the green bond index. Next, we established a fractional Brownian motion option pricing model with temporally variable volatility. We used this approach to predict the closing price of the China Bond–Green Bond Index from 3 January 2017 to 30 December 2021 as an empirical analysis. The trend of the index predicted by the ARMA–GARCH model was consistent with the actual trend and predictions of actual prices were highly accurate. The modified fractional Brownian motion option pricing model improved the pricing accuracy. Our results provide a policy reference for the development of a green financial derivatives market, and can accelerate the transformation of markets towards a more sustainable economic development model.

Financial technology, also known as Fintech, is transforming our daily life and revolutionizing the financial industry. Yet at present, consensus regarding the effect of Fintech on green bonds market is lacking. With novel data from China, this study documents robust evidence showing that Fintech development can significantly boost green bonds issuance. Further analysis suggests that this promotion effect occurs by empowering intermediary institutions and increasing social environmental awareness. Additionally, we investigate the heterogeneous effect and find that the positive relation is more pronounced for bonds without high ratings and in cities connected with High-Speed Railways network. The results call for the attention from policymakers and security managers to take further notice of Fintech utilization in green finance products.

This paper selects 346 green bonds issued in China from 2016 to 2021 as the sample, and the Propensity Score Matching (PSM) method is employed to confirm the existence of ‘green premium’ in the Chinese bond market. On this basis, data on internet media sentiment and print media sentiment are collected from ‘Sina Weibo’ and ‘China Important Newspaper Full Text Database’ by both Web Crawler Technology and Textual Analysis Methods to explore the impact and the mechanism of heterogeneous media sentiments on the ‘green premium’. The results show that both the optimism of internet media and print media can significantly promote the ‘green premium’ of green bonds, and the influence of print media sentiment on the ‘green premium’ is greater than that of internet media sentiment. In addition, the Bootstrap method verifies the mediating effect of print media sentiment in the influence of internet media sentiment on ‘green premium’, indicating that print media sentiment is an important transmission path. Moreover, the results of the heterogeneity test show that the more optimistic the media is, the more significant the ‘green premium’ effect is in the regions with higher institutional environments and financial subsidy policies. The ‘green premium’ of green bonds is most pronounced for higher levels of institutional environment and green bond preferential policies.

As the problem of global climate change becomes more severe, countries have proposed the goals of carbon capping and carbon neutrality. Green finance is an essential capacity support for achieving carbon peaking and carbon neutrality, and it can guide and stimulate social capital to invest in low-carbon industries and initiatives via marketbased mechanisms. Based on the panel data of Chinese prefecture-level cities from 2006 to 2020, this paper empirically examines the impact of green finance on carbon emission efficiency using a two-way fixed-effects model, conducts a regional heterogeneity analysis, and examines the threshold effect of economic development level and the mediating role of regional innovation. The results indicate that, first, green finance contributes significantly to the improvement of carbon emission efficiency, and second, the level of regional economic development has a double threshold effect on the contribution of green finance to the improvement of carbon emission efficiency. Third, regional innovation is an important green finance channel for influencing carbon emission efficacy. The sensitivity of carbon emission efficiency to the green finance index demonstrates an inverted U-shaped trend. Fifth, the importance of green finance sub-dimensions in relation to carbon emission efficacy is as follows: green support, green credit, green insurance, green investment, green equity, green bond, and green fund. These findings provide theoretical support for green finance's role in promoting co-carbon efficiency and are valuable for policy formulation.

It has been confirmed that sustainable investments contributing to environmental protection can benefit from the deterioration of air pollution, but this influence mechanism has not been fully discussed. This paper proposes a mediation model to study air pollution's influence on green bonds. Theoretically, air pollution raises public environmental awareness and perceptions of physical health risks, leading to increased public concern. Enhanced public concern drives investors' green preference and environmental responsibility, thus expanding green bond demand. Our studies show air pollution is significantly positive related to public concern. Public concern positively links with green bond investment willingness, resulting in increased volatility. The total positive effect of air pollution on green bond is partly absorbed by the effect of public concern. These findings confirm the mediation role of public concern. In addition, major crisis events (e.g., COVID-19) may hinder the mediation process by generating a negative trend and distracting the public.