Impact of energy poverty on public health: A non-linear study from an international perspective

Lee, Chien-Chiang; Yuan, Zihao

doi:10.1016/j.worlddev.2023.106444

Cited by 33 publications

(1 citation statement)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Macroeconomic variables related to origin and destination are frequently employed as traditional predictors of tourism demand, mainly including industrial production index, CPI, and exchange rate (Ayeh & Lin, 2011;Lee & Yuan, 2024). Besides, new data sources based on the internet, such as web traffic and search engine, effectively augment conventional data sources.…”

Section: Introductionmentioning

confidence: 99%

Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

You,

Huang,

Lee

2023

Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

It is widely recognized that the tourism industry is susceptible to crisis or natural disaster. Although some literature has studied the consequences of the crisis and disaster, there remains a lack of study on the effect of COVID‐19. Against this background, this paper investigates the tourist flow forecasting by adopting an advanced nonparametric mixed‐frequency vector autoregressions model using Bayesian additive regression trees. This is particularly suitable for forecasting the presence of extreme observations, for example, the COVID‐19 pandemic. We investigate tourism demand forecasting using a large number of predictors, including industrial production index, CPI, exchange rate, economic policy uncertainty, Google trends index, and COVID‐19 infection rate. The data used for this study relate to tourist flows in Chinese Hong Kong, Japan, and South Korea. Empirical study demonstrates that this novel model significantly outperforms the traditional mixed‐frequency vector autoregressions model to quarterly tourist flow forecasting. Therefore, this model can significantly enhance tourism forecast accuracy in the face of extreme events. This study contributes to the literature on tourism forecasting and provides policymakers with policy implications.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

You,

Huang,

Lee

2023

Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

show abstract

Green finance, environmental quality and technological innovation in China

Su,

Lee

2023

Int. J Fin Econ

View full text Add to dashboard Cite

This research analyses the impact of green finance development on the environmental quality of China. It utilises provincial panel data from 2006 to 2021 and takes technological innovation as the intermediary variable. Through the construction of a comprehensive index of green finance, technological innovation and environmental quality, we verify the hypothesis that green finance development does improve environmental quality. Taking innovation as the intermediary variable, this study examines the hypothesis that green finance enhances environmental quality by improving the level of innovation and also illustrates the importance of developing technology‐intensive industries. Heterogeneity analysis shows that different regions or different levels of green financial development have varying effects on environmental improvement. Finally, the findings validate the environmental Kuznets curve (EKC) by showing an inverted U‐shape link between green finance and environmental quality. The results herein offer a benchmark for policymakers to further strengthen green finance policy, increase the proportion of green investment and raise the support of green finance for technological innovation, thus improving environmental pollution and promoting sustainable economic development.

show abstract

Bank leverage and systemic risk: Impact of bank risk‐taking and inter‐bank business

Zhang,

Lee

2024

Int. J Fin Econ

View full text Add to dashboard Cite

In order to prevent and resolve systemic risk more effectively through deleveraging policy, this research takes China A‐share listed commercial banks from 2011 to 2021 as samples, calculates the systemic risk spillover through the conditional value at risk model, re‐estimates the leverage ratio with reference to the “Administration Measures for the Leverage Ratio of Commercial Banks (revised)”, and evaluates the influence of the leverage ratio on systemic risk. The results show that a high leverage ratio increases systemic risk spillover, and under this increase state‐owned commercial banks have the greatest contribution. Further research presents that a better leverage ratio enhances the systemic risk spillover by improving bank risk‐taking and encouraging inter‐bank business expansion. In addition, over different periods of economic development the influence of the leverage ratio on systemic risk exudes different characteristics. During an economic upswing, the leverage ratio's impact on systemic risk significantly drops.

show abstract

Impact of energy poverty on public health: A non-linear study from an international perspective

Cited by 33 publications

References 133 publications

Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

Green finance, environmental quality and technological innovation in China

Bank leverage and systemic risk: Impact of bank risk‐taking and inter‐bank business

Contact Info

Product

Resources

About