We investigate whether a firm’s decision on green bond issuances is influenced by the green bond issuances by other firms in the same industry. We find that a firm is significantly more likely to issue green bonds after observing that other firms in the same industry have previously issued green bonds. This effect cannot be explained by the issuer’s supplement to their previous issuances, incentive policies, and industry competition. Furthermore, we show that issuing green bonds can bring significant positive stock excess returns, which increases the motivation for institutional investors to learn and drive other firms in the same industry they hold to issue green bonds. Our findings indicate that the peer effect can be driven by social learning of the common ownership among firms and explain the reason for the rapid increase in green bond issuance.

Green bonds have emerged as a novel funding mechanism specifically aimed at addressing environmental challenges. Focusing on A-share listed companies in China that went public with bond issues domestically from 2012 to 2021, we reveal that companies with higher energy usage and better environmental disclosure quality are the most inclined to issue green bonds. Such issuance is identified as a pathway towards real green transformation, markedly boosting the green transformation index, green innovation efficiency, and ESG performance. Further analysis indicates that the effect of substantial transformation is particularly pronounced among companies in the eastern regions of China.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.

This paper examines bond trading activity and transaction cost differences between the bilateral Over-the-Counter (OTC) and the centralized Central Limit Order Book (CLOB) venues in the China interbank government bonds market, structured as all-to-all. Using a novel trade-level dataset, we estimate that CLOB reduces transaction costs by 0.66 basis points compared to OTC, highlighting the efficiency of its centralized trading mechanism. Furthermore, our analysis of cross-venue selection patterns reveals that the CLOB venue disproportionately facilitates core traders, orders with standardized sizes and settlement speeds, and newly issued bond trades. Despite CLOB’s cost advantages, the continued use of OTC is justified by its unique benefits, including mitigating information leakage, enabling designated counterparties, and facilitating position rebalancing. These findings offer insights into how market microstructure and trading mechanism affect asset liquidity.

This research examines the impact of short selling on the financing cost of corporate bonds using panel data from Chinese A-share listed companies spanning the period from 2007 to 2022. The study aims to investigate the potential cross-market information spillover effects within the short selling system. The findings indicate that short selling significantly reduces the financing cost of corporate bonds, with a more pronounced effect observed under greater short selling forces. The robustness of the results is confirmed by controlling for various potential influencing factors and addressing the endogeneity issue through Propensity Score Matched Difference in Differences (PSM-DID) methodology. Moreover, the research reveals that the alleviation of information asymmetry serves as the primary mechanism through which short selling exerts its impact, particularly in regions with well-developed financial markets and favorable legal environments. This study offersa novel perspective of short selling in China and it sheds light on its cross-market spillover effects. By effectively enhancing resource allocation efficiency in capital markets, short selling emerges as a potent tool for mitigating information disparities between bond investors and enterprises.

We investigate the effect of third-party certification for green bonds by analyzing its impact on issuer's future green innovation performances. We find that third-party certification for green bonds can significantly promote issuer's future green innovation performances. Furthermore, the promotion effect is more prominent in non-state-owned issuers, large issuers and heavy polluting issuers, and can be more significantly exerted by professional and reputable third-party certification agencies. Besides, third-party certification for green bonds can play the effect by reducing the issuer's tax expenditure, increasing the issuer's loan financing, and receiving a positive response in stock returns. But unexpectedly, it cannot play the effect by further reducing the credit spread of green bonds. Our findings indicate that independent external supervision can play a positive role in green bond issuance, but there is still a long way to go.

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.

Climate change and the escalation of extreme weather events jeopardize every corner of the globe. This paper investigates the impact of extreme high temperatures on the spread of newly issued municipal corporate bonds (MCBs) in China, which serves as a crucial instrument for local governments to meet the financial demands. We find that relative to the reference temperature range of 16 ◦C–20 ◦C, the issuing spread of MCBs increases by 2.48 basis points for each extra day where the mean temperature surpasses 32 ◦C. The findings highlight the risk-increasing effects of extreme temperatures in financial markets.

Biodiversity conservation incurs substantial economic costs. We investigate how financial markets price the risks such costs induce, exploiting the “Green Shield Action,” a major regulatory initiative launched in China in 2017 to enforce biodiversity preservation rules in national nature reserves. While improving biodiversity, the initiative led to significant increases in bond yields for municipalities with these reserves. The effects are driven by increases in local governments’ fiscal risk due to expected increases in transition costs resulting from shutting down illegal economic activities within reserves and additional public spending on biodiversity. Investors show little non-financial consideration towards endeavors counteracting biodiversity loss.