The sample data are used from Shaanxi Statistical Yearbook （1999-2018）and poverty population in X County of Southern Shaanxi in 2018. By using Eviews and Excel, this report focuses on analyzing the relationship between education investment and economic growth, education for poverty population, per capita income and poverty caused by education in X county, which can explain reasons for the phenomenon of poverty caused by education. The countermeasures are proposed to solve the phenomenon of poverty caused by education, including establishing scientific education view, correctly understanding education investment and benefit, and improving the aid mechanism for poor students to realize the effective connect among poverty alleviation through education, accurate poverty alleviation and rural revitalization.

This study explores the transformative potential of integrating Building Information Modeling (BIM) and Generative Design methodologies in heritage conservation and building management. By utilizing BIM, detailed architectural, structural, and MEP models were created, facilitating precise design and effective stakeholder collaboration. Generative Design enabled the exploration of multiple design solutions, optimizing spatial layouts and structural integrity. The project also integrated automated management systems and IoT sensors to enhance real-time monitoring, energy efficiency, and user comfort through the development of a digital twin. Despite encountering challenges such as technical complexities and budget constraints, the project successfully preserved the cinema's historical essence while incorporating modern functionalities. The findings highlight the contributions of BIM and Generative Design to the AEC industry, emphasizing their role in improving design accuracy, operational efficiency, and sustainability. This research provides valuable insights for future projects in heritage conservation, offering a blueprint for balancing historical preservation with contemporary needs. The revitalization of the "Ex Cinema Santa Barbara" in Paternò exemplifies these advancements, demonstrating how these technologies can restore and modernize culturally significant historical buildings effectively.

Using the 2011-2020 digital inclusive finance data released by Peking University and the index system constructed by the Blue Book of China Rural Revitalization and Development Index (2018), this paper analyzes the dynamic evolution of the digital inclusive finance and the rural revitaliza-tion from time and space dimensions with the help of kernel density estimation, Markov chain and Moran index. Using The Gini coefficient decomposition method to analyze the source of differ-ences in digital inclusive finance, and then using the spatial autoregressive (SAR) model and ge-ographically weighted regression (GWR) model to study the boosting effect of digital inclusive finance on rural revitalization from the perspective of spatial aggregation and spatial differentia-tion, respectively. The results show that: (1) The growth rate of China's digital inclusive finance slows down year by year, and the inter-provincial differences increase year by year and show gradient characteristics, indicating that there may be a trend of multipolar differentiation. The overall level of Rural Revitalization shows an increasing trend, and the gap between provinces is still apparent. (2) The evolution of digital inclusive finance and rural revitalization is a slow ad-justment process, and there has been no cross-level jump in the past ten years. In digital inclusive finance, the liquidity from the highest and lowest levels to the medium level is high. However, the liquidity in the states of rural revitalization development level is not high. (3) Rural revitalization has a positive spatial spillover effect. The level of rural revitalization in the western area is signif-icantly lower than in the eastern area. At the same time, there is no significant difference between the east and central areas. The depth of the use of digital inclusive finance has a significant positive impact on the revitalization and development of rural areas, indicating that the further promotion of digital inclusive finance business in rural areas can substantially boost the revitalization of rural areas. (4) The boosting effect of digital inclusive finance on rural revitalization shows prominent spatial differentiation characteristics. The depth of use and the degree of digitization generally show a positive impact. The central and eastern coastal cities have the highest impact, decreasing toward the southwest and northeast. The areas with the lowest usage depth impact are clustered in the northeast, and the areas with the lowest digitization impact are clustered in the southwest.

The United Nations Sustainable Development Goals call for the eradication of poverty, and China has proposed the Rural Revitalization Strategy based on the achievements of its poverty alleviation efforts in 2020. As a vital component of this strategy, the impact of land transfer on farmers' income growth has become a hot topic in both theoretical and practical sectors. This paper utilizes data from the CFPS database for the years 2010-2018 to construct OLS regression models and a Heckman two-stage model to analyze the effects of land transfers on the incomes of different types and sources among farmers. The empirical results show: First, land transfers do promote income growth among farmers, but the effect is primarily positive for those transferring out land, with negligible impact on those acquiring land. Second, the impact of land transfers varies between different types of income for transferees and transferors. Wage income contributes up to 88.26% to the income growth of transferors, a significantly higher rate than the decrease in business income; however, the increase in business income for transferees does not offset the decline in wage income, leading to no significant change in overall income. Further sensitivity analysis using the Heckman two-stage model confirms the robustness of these findings. The conclusions of this study provide theoretical and empirical evidence to optimize land transfer policies, enhance participation in land transfers, and ultimately achieve the mission of rural revitalization.

In Japan, where the working population is declining, there is a growing expectation that the potential working population will play an active role, and in this context, administrative measures such as the promotion of women's activities have been implemented. However, there is no end to the number of women who leave the workforce after life events such as childbirth or child rearing.　For example, in Fukuoka Prefecture (population 5.1 million) alone, where we are based, it is estimated that there are 43,300 potential female workers. Many of these women have have been employed in the past, but have left the workforce due to childbirth, family care, or other reasons, and still wish to work. However, although this group has potential employment needs, they have not yet engaged in specific job-hunting activities, and thus are on the periphery of the labor market, without access to the current active labor market. There are many promising potential employees there, and support for their career redevelopment and participation in the labor market is required. On the other hand, few studies have been conducted in Japanese prefectures to systematically estimate social value, i.e., to determine the scope of impact on society and the stakeholders. This is an obstacle to the formulation of policies for women's advancement and the establishment of related social projects. This paper is aware of this problem and, based on the support for women seeking to re-enter society (career redevelopment) that the authors are implementing, selects seven prefectures in Kyushu, which is also a wide economic zone, as a support area and estimates the social value to companies and families in these areas by using SROI. As a result, it was derived that if 3,586 individuals were reemployed, the SROI ratio (1:X) X would be 76.1 in the first year of reintegration assistance activities and X would be 313.3 in the fourth year.