Introduction: China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China.Methods: This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency.Results: The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran’s Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces’ values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume.Discussion: To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Objective Analysing and evaluating how efficiently health resources are allocated to county-level Traditional Chinese Medicine (TCM) hospitals in Zhejiang Province, this study aims to provide empirical evidence for improving operational efficiency and optimising resource allocation in these hospitals. Design and setting The study employed a three-stage Data Envelopment Analysis (DEA) model to assess efficiency, using data from 68 county-level TCM hospitals. Four input and five output variables related to TCM services were selected for the analysis. Results The first-stage DEA results indicated that in 2022, the technical efficiency (TE) of TCM hospitals in Zhejiang Province was 0.788, the pure technical efficiency (PTE) was 0.876 and the scale efficiency (SE) was 0.903. The classification of hospitals into four groups based on the bed size showed statistically significant differences in returns to scale (p East (0.844) > South (0.805) > North (0.796) > Central (0.731). Conclusion There is an imbalance between the inputs and outputs of county-level TCM hospitals. Each region must consider factors such as the local economy, population and medical service levels, along with the specific development characteristics of hospitals, to reasonably determine the scale of county-level TCM hospital construction. Emphasis should be placed on improving hospital management and technical capabilities, coordinating regional development, promoting the rational allocation and efficient use of TCM resources and enhancing the efficiency of resource allocation in county-level TCM hospitals.

According to the Paris Agreement signed on 12 December 2015, forests have great impact on controlling CO₂ emissions. This study uses the nonoriented dynamic slack‐based data envelopment analysis model to evaluate the impact of a forestry area on the annual and overall energy efficiency in 28 EU countries during 2009‐2016. We found that annual and overall energy efficiency improved slightly and the energy efficiency in each country also changed when the forestry area was used as the output variable. Although the total energy consumption has declined slowly, CO₂ emissions have not declined effectively. Thus, it will be challenging for the EU to meet its target of cutting CO₂ emissions by 30% by 2030. All countries should take additional measures, such as energetically increasing forestry areas, optimally distributing the labor force among industries, and gradually replacing fossil fuels with renewable energy to improve their energy efficiency.

In order to have a sustainable economic and social development, it is important to balance economic growth and ecological environmental damage. In this article, we used the resampling model under the triangular distribution to evaluate energy efficiency, because the input/output value may have measurement errors, time lag factors, arbitrariness, and other problems, causing their own DMU to change. After these factors were taken into consideration, the resampled input/output was estimated because a super-SBM efficiency value was placed in the confidence interval. From the past-present data, for the estimated data change, the time weight was provided according to the Lucas series, and the super-SBM was time-weighted. We applied this model to a dataset of G20 economies from 2010 to 2014. To the best of our knowledge, very few studies have applied the DEA method with resampling to analyze energy efficiency. Thus, our study contributes to the methodologies for energy efficiency evaluation. We found that the overall average energy efficiency is 0.653, with substantial differences between developed economies and developing economies. The most important finding is that neither overestimation nor underestimation occurred when sampling was repeated one thousand times using 95% and 80% confidence intervals, confirming the robustness of the super-SBM model. The less energy-efficient economies should adjust their energy policies appropriately and develop new clean energy technologies in the future.

Background: In order to regulate the surging medical spending, the Chinese government conducted a reform of medical insurance payment directed to regional global budgets and Diagnosis-Intervention Packet (DIP) system. Methods: An interrupted time series analysis was conducted using monthly medical insurance claims data from 45,900 T2DM inpatients in S City, China between January 2020 and December 2023, comparing outcomes before and after the January 2021 DIP reform implementation across insurance types (UEBMI vs. URRBMI) and hospital types (TCM vs. general hospitals). Results: The DIP reform significantly reduced total hospitalization costs and length of stay overall, but divergently affected out-of-pocket ratios—decreasing them for UEBMI enrollees while significantly increasing them for URRBMI patients and TCM hospital patients—indicating worsened financial equity for vulnerable subgroups despite improved cost. Conclusion: While the DIP payment reform effectively reduced hospitalization costs and length of stay for T2DM patients, it simultaneously exacerbated out-of-pocket inequities across insurance schemes and hospital types, indicating that future payment policies must integrate cost containment with health equity.