The Chinese government is implementing a rural revitalization strategy and speeding up rural modernization. The rapid development of the rural digital economy has become a new driving force for the revitalization of rural industries. By analyzing how rural industrial revitalization is driven by the rural digital economy, this study constructs a rural digital economy from the three dimensions of optimal allocation of rural resource elements, the effective connection of urban–rural markets, and the integrated development of rural industries. The entropy value method was used to assess the comprehensive level of development of the rural digital economy and industrial revitalization. Based on the agricultural and rural development data of 30 provinces in China from 2014 to 2019, we construct a dual fixed effect model to empirically analyze how the rural digital economy drives rural industrial revitalization. The results show that, first, the rural digital economy promotes the revitalization of rural industries through the following main approaches: optimal allocation and utilization of rural resources, effective connection of urban–rural markets, and industrial integration development. Second, using the benchmark regression test, it can be concluded that if the development level of the rural digital economy is increased by 1 percent, the development of rural industrial revitalization will increase by 0.066 percent, indicating that the development of the rural digital economy can significantly drive the revitalization of rural industries. Third, the driving effect of the rural digital economy on the revitalization of rural industries is heterogeneous in different regions and industrial structures. The effect of the rural digital economy on the revitalization of rural industries in the western region, the region with a high proportion of the output value of the primary industry, is higher than in the east and central regions, which have a low proportion of the output value of the primary industry.

Digital innovation ecosystems are currently experiencing a period of growth and are navigating uncertain environments. Improving resilience is an important prerequisite for ensuring sustainable developments. This study, based on the technology, organization, and environment (TOE) framework, examines the impact of multilevel antecedent conditions on digital innovation ecosystem resilience using data from 31 Chinese provinces. By applying a necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA), this study reveals complex causal relationships between five antecedents at the “technology–organization–environment” levels and digital innovation ecosystem resilience, along with the improvement paths of digital innovation ecosystem resilience. The results show the following: Firstly, individual antecedent conditions alone do not constitute necessary conditions for high or non-high digital innovation ecosystem resilience. Secondly, there are five configuration paths leading to high digital innovation ecosystem resilience, namely, a digital technology-enabled organization–environment-driven type (H1a), an organization–environment dual-wheel-driven type (H1b), a digital technology-led environment-driven type (H2), a technology–organization–environment trilateral type (H3), and a pressure–organization-driven type (H4). Thirdly, three configuration paths result in non-high digital innovation ecosystem resilience, exhibiting an asymmetric relationship with paths associated with the configuration paths of high digital innovation ecosystem resilience. Finally, potential substitution relationships exist among antecedent conditions at the technological, organizational, and environmental levels.

Examination of livelihood assets is crucial for displaced people as it informs effective resource allocation and support. This study investigates the livelihood decisions of households displaced by Nepal's Tanahu Hydropower Project using multinomial logistic regression to evaluate how different assets affect their choices. Data were obtained using questionnaires, with a total of 185 questionnaires used for the analysis in this study. Descriptive and inferential statistics were used for data analysis. This study finds that physical assets substantially influence livelihood strategies, promoting diversification and nonfarming activities. Interestingly, social assets have a negative impact, whereas natural, financial, and human assets exert no significant effect. The study highlights the community's focus on economic stability, balancing immediate financial needs with long-term goals such as children's education. However, it reveals ongoing financial struggles, with an average daily income of only 1.962 USD and many households remaining in poverty, particularly those dependent on farming. The study also reflects on the mixed perceptions toward government policies, influenced by factors such as skill gaps and family aspirations. These findings provide essential insights for targeted support and resource allocation to displaced populations.

Optimizing higher education resource allocation in western China is vital for advancing national development through education and talent. This research covers the DEA Malmquist to examine the effectiveness of higher education materials distributed statically and vigorously within twelve provinces in the western part of China. It also studies the internal inequalities in resource distribution effectiveness and employs the Tobit model to identify which the main factors affecting the efficiency of higher education resource allocation. The primarily data sources from the China Education Yearbook (2011–2021). The findings indicate that the comprehensive technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) have not reached the efficiency frontier in higher education resource allocation in western China. Conversely, the dynamic analysis reveals a decline in overall efficiency in resource allocation for higher education in the western region, with significant variations in efficiency levels among the provinces. Factors such as education expenditure, GDP per capita, total GDP, and the breadth of education significantly impact the efficiency of resource allocation for higher education in the western region. To improve this efficiency, it is essential to boost financial input into education, adjust resource allocation strategies, focus on matching educational quality with market demands, and implement dynamic monitoring and evaluation.

This paper presents research on the hydropower-displaced communities of the Middle Bhotekoshi and Arun-III projects in Nepal, focusing on the effects of different livelihood assets on their livelihood strategies. The study finds that certain assets, such as social and human capital, significantly affect livelihood strategy selection, while others, such as physical and financial capital, do not. Additionally, most households (66%) diversified their income away from agriculture. Although non-farm sources were the most viable strategy, the majority (67.77%) of people living below the international poverty line also used this strategy. The paper also explores the impact of the COVID-19 pandemic on hydropower-displaced people in Nepal, highlighting key challenges such as reduced household income, disrupted agricultural production, and inadequate access to medical care. Ultimately, the study underscores the need for targeted interventions to address the specific needs of hydropower-displaced people in Nepal, considering both their displacement and the ongoing pandemic.