Central bank digital currency (CBDC) is intended to boost financial inclusion and limit threats to bank stability posed by private cryptocurrencies. Our study examines the impact of implementing CBDC on the bank stability of two countries in Asia and the Pacific, the People’s Republic of China (PRC) and India, that initiated research on CBDC within the last ten years (2013 to 2022). We construct a bank stability index by utilizing five dimensions, namely capital adequacy, profitability, asset quality, liquidity, and efficiency, using a novel “benefit-of-the-doubt” approach. Employing panel estimation techniques, we find a significant positive impact of adopting CBDC on bank stability and a moderating role of monetary policy. We also find that the effect is greater in India, a lower-middle-income country, than in the PRC, an upper-middle-income nation. We conclude that by taking an accommodative monetary policy stance, adopting CBDC favors bank stability. We confirm our results with various robustness tests by introducing proxies for bank stability and other model specifications. Our findings underscore the potential of adopting CBDC, when carefully managed alongside appropriate monetary policy, for enhancing bank or overall financial stability.

We study tail-risk contagion in Belt and Road (BRI) stock markets by conditioning on shocks from China and global commodities. We construct time-varying contagion indices from conditional higher co-moments (CoHCM) estimated within a DCC-GARCH model with generalized hyperbolic innovations, and apply them to daily data for 32 BRI markets. The higher-moment index isolates two channels: a China-driven financial-institutional channel and a WTI-driven commodity-real-economy channel, whereas a covariance benchmark fails to recover this separation. Furthermore, the system-GMM estimates link the China-conditional channel to institutional quality and financial depth, and the WTI-conditional channel to real activity. In out-of-sample portfolio tests, the WTI-conditional signal improves risk-adjusted performance relative to equally weighted and mean-variance benchmarks, while the China-conditional signal does not. Tail-based measurement thus sharpens identification of contagion paths and yields information that is economically relevant for risk management in interconnected emerging markets.

Background: How to cope with the rapid growth of LTC (long-term care) needs for the old people without activities of daily living (ADL), which is also a serious hazard caused by public health emergencies such as COVID-2019 and SARS (2003), has become an urgent task in China, Germany, Japan, and other aging countries. As a response, the LTCI (longterm care insurance) system has been executed among European countries and piloted in 15 cities of China in 2016. Subsequently, the influence and dilemma of LTCI system have become a hot academic topic in the past 20 years.Methods: The review was carried out to reveal the effects of the LTCI system on different economic entities by reviewing relevantliterature published from January 2008 to September 2019. The quality of 25 quantitative and 24 qualitative articles was evaluated using the JBI and CASP critical evaluation checklist, respectively. Results: The review systematically examines the effects of the LTCI system on different microeconomic entities such as caretakers or their families and macroeconomic entities such as government spending. The results show that the LTCI system has a great impact on social welfare. For example, LTCI has a positive effect on the health and life quality of the disabled elderly. However, the role of LTCI in alleviating the financial burden on families with the disabled elderly may be limited. Conclusion: Implementation of LTCI system not only in reducing the physical and mental health problems of health care recipients and providers, and the economic burden of their families, but also promote the development of health care service industry and further improvement of the health care system. However, the dilemma and sustainable development of the LTCI system is the government needs to focus on in the future due to the sustainability of its funding sources.

This paper explores the impact of Confucian culture on the environmental management practices of firms, utilizing data from A-share listed companies in China from 2009 to 2022. The study reveals several significant findings: (1) Firms in regions with a stronger presence of Confucian culture are more likely to adopt environmentally responsible management practices; (2) Confucian culture enhances firms' environmental management through three channels: promoting innovation, easing financing constraints, and reducing managerial myopia, with particular emphasis on alleviating financing constraints; (3) Regional environmental regulations mitigate the positive influence of Confucian culture on firms' environmental management practices. This study contributes to the literature by elucidating the determinants of corporate environmental management and emphasizing the critical role of cultural factors, particularly in overcoming financial barriers, in corporate decision-making.

The commodity and financial characteristics of carbon emission allowances play a pivotal role within the Carbon Emission Trading Scheme (CETS). Evaluating the effectiveness of the scheme from the perspective of carbon price is critical, as it directly reflects the underlying value of carbon allowances. This study employs a time-varying Difference-in-Differences (DID) model, utilizing data from publicly listed enterprises in China over the period from 2010 to 2023, to examine the effects of carbon price level and stability on Total Factor Productivity (TFP). The results suggest that both an increase in carbon price level and stability contribute to improvements in TFP, particularly for heavy-polluting and non-stateowned enterprises. Mechanism analysis reveals that higher carbon prices and stability can stimulate corporate engagement in green innovation, activate the Porter effect, and subsequently enhance TFP. Furthermore, optimizing the system environment proves to be an effective means of strengthening the scheme's impact. The study also finds that allocating initial quotas via payment-based mechanisms offers a more effective design. This research highlights the importance of strengthening the financial attributes of carbon emission allowances and offers practical recommendations for increasing the activity of trading entities and improving market liquidity.

The growing significance of sustainable development has underscored the importance of integrating corporate sustainability indicators into corporate strategies. As external stakeholders increasingly emphasize corporate environmential performance, social responsibility and governance (ESG), understanding its impact on corporate value becomes essential, especially in emerging markets like China. This research aims to bridge these knowledge gaps by empirically investigating the influence of ESG ratings on firms’ value among Chinese listed firms, with a special emphasis on the mediating roles played by financial distress and financing constraints. By analyzing data from listed companies of China over the period 2018 to 2022, this research explores the correlation between firms’ value and ESG ratings. The findings indicate a positive association between firms’ value and ESG ratings. Enhanced ESG ratings directly boost market valuation and indirectly elevate firm value by mitigating financing constraints and financial distress. Further analysis reveals the positive effects of ESG ratings are more noticeable in industries that are not heavily polluting and in state-owned enterprises. This research provides valuable insights for enterprise management by systematically examining how ESG ratings contribute to corporate value through the mitigation of financial distress and constraints, while also highlighting the variations in ESG strategy implementation across different types of enterprises.

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.

The 16th annual China International Conference on Insurance and Risk Management (CICIRM 2026) will be held on July 8-11, 2026 at the Yunnan Lianyun Hotel in Kunming, Yunnan, China. The conference is organized by the China Center for Insurance and Risk Management, School of Economics and Management, Tsinghua University, and co-organized by the School of Finance, Yunnan University of Finance and Economics.

We construct a new geospatial measure using high-resolution river vector data from National Geomatics Center of China (NGCC) to study how urban riparian exposure shapes local government debt financing costs. Our base-line results show that cities with higher riparian exposures have significantly lower credit spreads, with a one-standard-deviation increase in riparian exposure reducing credit spreads by approximately 12 basis points. By comparing cities crossed by natural rivers with those intersected by artificial canals, we disentangle the dual role of riparian zones as sources of natural capital benefits (e.g., enhanced transportation capacity) versus climate risks (e.g., flood vulnerability). We find that climate change has amplified the impact of natural disasters, such as floods and droughts, particularly in riparian zones, thus weakening the cost-reducing effect of riparian exposure on bond financing. In contrast, improved water infrastructure and flood-control facilities strengthen the cost-reduction effect. Our findings contribute to the literature on natural capital and government financing, offering valuable implications for public finance and risk management.

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.