This paper examines and confirms the varying volatility of the relationship between cryptocurrency and currency markets at different time periods, such as when the market encountered multiple risk events including the US–China trade war, COVID-19, and the Russian–Ukraine war. We employ the Diagonal BEKK model and find that the co-volatility spillover effects between the returns of cryptocurrencies and currencies, with the exception of Tether and the U.S. dollar index, evolved significantly. Furthermore, the co-volatility spillover effects between cryptocurrencies and EUR have the largest effects and fluctuations. Large-cap cryptocurrencies (Bitcoin and Ethereum) have greater co-volatility spillover effects between them and currencies. Regarding the ability of cryptocurrencies to act as safe-haven for currencies, we observe that Bitcoin, Ethereum, and Tether served as safe-havens during the US–China trade war, and Bitcoin was a safe-haven during COVID-19. During the 2022 Russian–Ukraine war, Bitcoin and Tether were safe-havens. Interestingly, our findings point out that Bitcoin provides a more consistent safe-haven function for currency markets. Overall, by including multiple global risk events and a comprehensive dataset, the results support our conjecture (and earlier studies) indicating that the capabilities of cryptocurrency are time-varying and related to market status and risk events with different natures.

Most meteorological information services in Taiwan are currently provided by the Central Weather Bureau, Ministry of Transportation and Communications. As agricultural production activities are sensitive to weather and climate conditions, meteorological information services are more important for agricultural decision-makers than those in other sectors. This study uses the contingent valuation method to estimate the economic value of meteorological information services in Taiwan for agricultural producers. We assess the agricultural producers’ willingness to pay (WTP) for the meteorological information services, conducting a national face-to-face survey of 400 registered farmers in 20 municipalities in Taiwan in 2013. The results show the adjusted WTP for every agricultural household each year with a 95% confidence interval which ranges from 56.06 US dollars to 90.92 US dollars. The inferred annual economic value of meteorological information services for agricultural producers in Taiwan is between 28.06 million US dollars and 45.51 million US dollars. Moreover, the agricultural producers’ subjective assessment of weather forecast accuracy, farm size, and first bid price significantly affect the amount agricultural producers are willing to pay for meteorological information services.

The rapid growth of cryptocurrencies, characterized by high volatility, weak correlations with traditional assets, and substantial environmental impacts, has spurred interest in their interactions with energy and green financial markets. This study employs a Markov-switching generalized autoregressive conditional heteroscedasticity model with dynamic conditional correlation to examine the regime-dependent return and volatility connectedness between dirty and clean cryptocurrencies and energy and green financial assets. The empirical results reveal regime dependence in cryptocurrency dynamics, as well as differences in return and volatility connectedness across asset classes. These findings underscore the importance of accounting for regime-dependent dynamics when evaluating cross-market risk transmission and diversification properties between cryptocurrencies, energy markets, and green financial assets.

Firm-level carbon emissions, while directly measurable, are often reported with delays or incomplete information, particularly for firms not subject to mandatory disclosure. Understanding and predicting the risks of high carbon emissions is therefore essential for financial analysis in economies pursuing sustainability. This study investigates the relationship between carbon emissions and financial ratios using firm-level data from the Taiwan Economic Journal for the period 2005 to 2020. The empirical findings reveal that financial ratios can serve as effective predictors of high carbon emission risk, with clear industry-specific differences. While the relevant predictors differ across industries, our results suggest that the receivables-to-sales ratio may serve as a useful early-warning financial indicator for identifying firms at greater risk of high carbon emissions. Overall, the study develops a cost-effective and forward-looking early-warning mechanism that enables governments, investors, and companies to manage carbon-related risks by leveraging readily available financial information.