Studies have argued that water risk affects corporate sustainability, but few of them have fully explored whether or not and how water resources have a direct impact on corporate finance and strategy. This study takes the listed companies in the Chinese A-share market from 2019 to 2023 as a sample to understand the threat of water vulnerability to corporate sustainability from the perspective of capital cost. This study argues that water vulnerability positively relates to corporate capital cost by increasing corporate financing constraints. Meanwhile, this study also examines the role of water regulation and water investment in the relationship between water vulnerability and corporate capital cost. Water regulation brings legitimate pressure to corporations and increases the transformation risks faced by them, so it has a positive moderating effect. Water investment can alleviate the vulnerability of local water resources and reduce the physical water risk faced by corporations, so it has a negative moderating effect. The study finds that the two measures mainly play a significant moderating effect on the cost of debt. In addition, the study finds that the positive relationship between water vulnerability and capital cost has industrial and firm-level heterogeneity, while the moderating effect of government water governance has only industrial heterogeneity.

Water resources are important factors limiting social and economic development, so how to ensure the coordination between economic development and water resources–ecological management capacity has become a key issue that needs to be addressed urgently for China’s high-quality economic development. This paper used nighttime light data as proxy variables of economic development to calculate the coupling coordination degree between provincial economic development and water resources–ecological management capacity in China from 2004 to 2019 based on the coupling coordination degree model; w constructed a spatial econometric model to explore the spatial correlation and influencing factors between economic development and water resources–ecological management capacity. The results are shown in the following: (1) The overall level of China’s economic development is on an upward trend, but the regional development is unbalanced, showing a decreasing spatial pattern distribution of the eastern coastal region–mid-western region–far-western region. (2) The level of water resources–ecological management capacity is low, and the spatial distribution shows a decreasing trend in the far west–central and western–eastern coastal regions. (3) The level of coupling and coordination between economic development and water resources–ecological management capacity rises from a mild imbalance level to a little imbalance level, and the spatial distribution is consistent with the spatial distribution of economic development. (4) The factors influencing the level of coupling and coordination of inter-provincial economic development and water resources–ecological management capacity in China mainly involve the population scale, technological progress, affluence, and foreign direct investment. Each province and city should take into account its own actual situation and develop targeted measures to promote the coordinated development of economic development and the water resources–ecological management capacity.

To address water sustainability challenges, this study investigates how public water concern influences corporate water responsibility (CWR) and how managerial green cognition moderates this relationship. Drawing on institutional theory and cognitive theory, we analyze a panel of 1292 publicly listed high-water-consuming firms in China from 2015 to 2024. The results show that public water concern significantly improves CWR by increasing legitimacy pressure, while its effect through government water governance attention is not statistically significant. Furthermore, managerial green cognition—including both economic and moral dimensions—positively moderates this relationship. Heterogeneity analysis reveals that the moderating effect is stronger in firms with more female directors, older executives, and internationally experienced teams. These findings contribute to refining institutional theory in the context of environmental responsibility and highlight the critical role of executive cognition and demographic structure in corporate sustainability behavior.

As freshwater scarcity becomes increasingly severe under climate change, physical water risk has emerged as a critical financial concern for firms in water-intensive industries. This study explores whether and how physical water risk influences firms’ cost of equity, and whether corporate social responsibility (CSR)—both its overall level and structural differentiation—modulates this relationship. Using panel data from 849 Chinese listed companies in water-intensive sectors between 2011 and 2022, we find that physical water risk significantly elevates equity capital costs. While a strong CSR performance buffers this effect, CSR differentiation—reflected in uneven CSR engagement across different domains—undermines or even reverses this moderating role. Additional heterogeneity analyses show that these patterns are more pronounced in large and non-state-owned enterprises. These findings deepen our understanding of how environmental risks are priced in capital markets and offer strategic insights for firms seeking to manage sustainability-related financial exposures.

Both academia and industry believe that introducing textual features into a financial distress prediction model can improve its accuracy. However, the textual features introduced by the research are relatively singular and fail to reflect the overall situation of the text comprehensively and effectively. Based on the traditional Z-score financial indicators model, four textual features of MD&A are introduced, namely, sentiment tone, text readability, forward-looking depth, and performance self-attribution. Using logistic regression, BP neural network, and deep belief network, empirical research draws conclusions from listed companies in China. The results show that the financial distress prediction model of listed companies considering multitextual features can effectively improve the prediction accuracy, and deep belief network has the potential to perform better prediction.