Objective: We aim to estimate the total factor productivity and analyze factors related to the Chinese government’s health care expenditure in each of its provinces after its implementation of new health care reform in the period after 2009.Materials and methods: We use the Malmquist DEA model to measure efficiency and apply the Tobit regression to explore factors that influence the efficiency of government health care expenditure. Data are taken from the China statistics yearbook (2004–2020).Results: We find that the average TFP of China’s 31 provincial health care expenditure was lower than 1 in the period 2009–2019. We note that the average TFP was much higher after new health care reform was implemented, and note this in the eastern, central and western regions. But per capita GDP, population density and new health care reform implementation are found to have a statistically significant impact on the technical efficiency of the provincial government’s health care expenditure (P<0.05); meanwhile, region, education, urbanization and per capita provincial government health care expenditure are not found to have a statistically significant impact.Conclusion: Although the implementation of the new medical reform has improved the efficiency of the government’s health expenditure, it is remains low in 31 provinces in China. In addition, the government should consider per capita GDP, population density and other factors when coordinating the allocation of health care input.Significance: This study systematically analyzes the efficiency and influencing factors of the Chinese government’s health expenditure after it introduced new health care reforms. The results show that China’s new medical reform will help to improve the government’s health expenditure. The Chinese government can continue to adhere to the new medical reform policy, and should pay attention to demographic and economic factors when implementing the policy.

In 2011, the Chinese government launched a disaster mitigation and preparedness program called the Resettlement of South Shaanxi (RSS). Due to the wide geographical scope and complex interests, the possibility of conflicts was increased during and after resettlement. Efficient risk communication improves the supply of information about risks and meets the risk-related information needs of individuals. Using the risk information seeking and processing (RISP) model, this research applied a structural equation model and survey with a structured questionnaire to study ways to improve risk communication in disaster resettlement. A total of 616 valid questionnaires were provided by study respondents in resettlement sites in Ziyang County, Ankang City, Shaanxi Province. The results indicated the following: (1) the public’s information seeking behavior relies more on village committees and village officials than other channels. Emerging information channels, such as Weibo and WeChat (social media applications in China), do not play leading roles in disseminating risk information. (2) There are differences between the information channels used by residents and the channels that residents believe the most. (3) Relevant channel beliefs, information sufficiency, perceived hazard characteristics, and self-efficacy directly influence risk information seeking behavior. However, the capacity to gather information has non-significant direct influences on information seeking behavior. (4) Perceived hazard characteristics and self-efficacy drive risk information seeking behavior in both direct and indirect ways through information sufficiency.

Flood-induced displacement has become a critical challenge in disaster-prone areas particularly in low-income countries where communities depend entirely on the natural resources. This study examines how various forms of livelihood capital such as natural, physical, human, social and financial capital affect livelihood resilience among flood-displaced households, and the mediating role of coping behaviors. Using the Sustainable Livelihood Framework and resilience theory, the study employs partial least squares structural equation modeling (PLS-SEM) to examine the survey data from 590 displaced farming households. Findings show that flood-induced displacement significantly depletes livelihood capitals, thereby constraining households’ adaptive capacity. Social and natural capitals exhibit the strongest relative contributions to livelihood resilience, followed by financial and human capital, whereas physical capital shows a comparatively weaker effect. Coping behavior serves as a key mediating mechanism that transforms available capital into resilience outcomes. The study further proposes capital ranking strategies as an innovative analytical tool to compare the relative influence of different livelihood capitals rather than as a prescriptive prioritization approach. By reconceptualizing coping behavior as a mediator variable, rather than an outcome variable, this study contributes to the livelihood resilience theory. It offers empirically grounded insights for policymakers, development practitioners, and humanitarian agencies designing targeted resettlement, sustainable intervention, and recovery initiatives.