For the whole world, poverty problem has persisted for a long time. To study the impact of tourism polices on poverty alleviation, this dissertation employs a literature review and case analysis approach. On the basis of reviewing relevant research literature, choosing Guizhou province in China as a case study, conducting in-depth study. The result revealed that national tourism poverty alleviation policy for Guizhou province effectively reduced the gap between urban and rural disposable income in Guizhou province. For urban residents, tourism policies have significant improvement in net income from individual operations and net property income growth. For rural residents, tourism policies have improvement in per capita disposable income of permanent rural residents, wage income, net business income, net property income and net transfer income. Baiyun District has the smallest rural-urban gap. However, because poverty alleviation is the result of multidimensional and multifaceted collaborative efforts, the promulgation of tourism policies may be just one of the factors. In addition to this, factors such as relevant economic policies and technological advancements also exert an influence. As a result, poverty alleviation efforts should be tailored to local conditions and areas with different geographical conditions should employ different approaches.

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Digital transformation has injected core momentum into industrial integration. Based on four dimensions including digital technology innovation, this paper constructs a comprehensive evaluation index system covering five dimensions, uses the coupling coordination model to measure the integration level of the two industries in 29 provinces in China from 2009 to 2024, and analyzes the heterogeneity from three dimensions: time, space, and industry. The research finds that: the integration of the two industries has steadily climbed, with the coordination level advancing from barely coordinated to moderately coordinated; spatially, it presents a pattern of "southeastern leadership and inland catch-up", forming three radiation centers; industries such as education are leading in integration with advanced manufacturing, while industries such as finance are slower in integration. Based on this, four suggestions are put forward, including optimizing the regional layout and promoting the differentiated development of industries, to provide reference for the in-depth integration of the two industries and empowering high-quality economic development.

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This study uses A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2011 to 2024 as a sample to investigate the impact of corporate ESG performance on equity financing costs. The results indicate that improved ESG performance significantly reduces the cost of equity financing, and this conclusion remains robust after variable replacements and the exclusion of samples from the COVID-19 period. Commercial credit and earnings per share serve as transmission mechanisms between ESG performance and equity financing costs, while the role of stock liquidity follows a specific logic. Heterogeneity analysis shows that the effect is more pronounced in non-polluting firms, firms outside the eastern region, non-asset-intensive firms, and firms whose executives have a financial background.

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This paper looks at how systematic risk—measured through beta coefficients—behaves differently depending on whether we use daily or monthly return data. We pick nine major industry sectors and run standard market model regressions at both frequencies over a three-year window (March 2017 to March 2020, using S&P 500 as the benchmark). What we find is pretty striking: for most sectors, the sign of the average beta actually flips between daily and monthly estimates. Daily betas tend to be noisy and jump around a lot, while monthly ones give much smoother trajectories. These results suggest that anyone relying on just one frequency for risk assessment might be getting an incomplete—or even misleading—picture. We also argue that simple OLS-based approaches, rather than fancy machine learning models, work quite well here precisely because complex models tend to overfit the short-term noise.

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